Overview

Dataset statistics

Number of variables75
Number of observations100
Missing cells843
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory601.3 B

Variable types

Numeric34
Text10
Categorical17
Unsupported6
DateTime3
Boolean5

Alerts

scrape_id has constant value ""Constant
host_has_profile_pic has constant value ""Constant
amenities has constant value ""Constant
has_availability has constant value ""Constant
calculated_host_listings_count_shared_rooms has constant value ""Constant
source is highly imbalanced (56.4%)Imbalance
host_location is highly imbalanced (67.5%)Imbalance
host_response_rate is highly imbalanced (56.7%)Imbalance
host_verifications is highly imbalanced (63.1%)Imbalance
neighbourhood is highly imbalanced (60.9%)Imbalance
description has 100 (100.0%) missing valuesMissing
neighborhood_overview has 43 (43.0%) missing valuesMissing
host_location has 3 (3.0%) missing valuesMissing
host_about has 4 (4.0%) missing valuesMissing
host_response_time has 13 (13.0%) missing valuesMissing
host_response_rate has 13 (13.0%) missing valuesMissing
host_acceptance_rate has 16 (16.0%) missing valuesMissing
neighbourhood has 43 (43.0%) missing valuesMissing
neighbourhood_group_cleansed has 100 (100.0%) missing valuesMissing
bathrooms has 100 (100.0%) missing valuesMissing
bedrooms has 100 (100.0%) missing valuesMissing
price has 2 (2.0%) missing valuesMissing
calendar_updated has 100 (100.0%) missing valuesMissing
has_availability has 2 (2.0%) missing valuesMissing
first_review has 10 (10.0%) missing valuesMissing
last_review has 10 (10.0%) missing valuesMissing
review_scores_rating has 10 (10.0%) missing valuesMissing
review_scores_accuracy has 10 (10.0%) missing valuesMissing
review_scores_cleanliness has 10 (10.0%) missing valuesMissing
review_scores_checkin has 10 (10.0%) missing valuesMissing
review_scores_communication has 10 (10.0%) missing valuesMissing
review_scores_location has 10 (10.0%) missing valuesMissing
review_scores_value has 10 (10.0%) missing valuesMissing
license has 100 (100.0%) missing valuesMissing
reviews_per_month has 10 (10.0%) missing valuesMissing
id has unique valuesUnique
listing_url has unique valuesUnique
picture_url has unique valuesUnique
description is an unsupported type, check if it needs cleaning or further analysisUnsupported
neighbourhood_group_cleansed is an unsupported type, check if it needs cleaning or further analysisUnsupported
bathrooms is an unsupported type, check if it needs cleaning or further analysisUnsupported
bedrooms is an unsupported type, check if it needs cleaning or further analysisUnsupported
calendar_updated is an unsupported type, check if it needs cleaning or further analysisUnsupported
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
availability_30 has 25 (25.0%) zerosZeros
availability_60 has 20 (20.0%) zerosZeros
availability_90 has 19 (19.0%) zerosZeros
availability_365 has 12 (12.0%) zerosZeros
number_of_reviews has 10 (10.0%) zerosZeros
number_of_reviews_ltm has 31 (31.0%) zerosZeros
number_of_reviews_l30d has 64 (64.0%) zerosZeros
calculated_host_listings_count_entire_homes has 18 (18.0%) zerosZeros
calculated_host_listings_count_private_rooms has 68 (68.0%) zerosZeros

Reproduction

Analysis started2024-04-28 18:03:51.205351
Analysis finished2024-04-28 18:05:42.733410
Duration1 minute and 51.53 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225008.94
Minimum17878
Maximum404674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:42.835233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile48871.2
Q188717.75
median249039.5
Q3334104.25
95-th percentile391856.6
Maximum404674
Range386796
Interquartile range (IQR)245386.5

Descriptive statistics

Standard deviation121200.16
Coefficient of variation (CV)0.53864598
Kurtosis-1.419796
Mean225008.94
Median Absolute Deviation (MAD)101484.5
Skewness-0.26980255
Sum22500894
Variance1.4689479 × 1010
MonotonicityNot monotonic
2024-04-28T15:05:42.993776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17878 1
 
1.0%
87762 1
 
1.0%
262466 1
 
1.0%
361569 1
 
1.0%
88781 1
 
1.0%
361568 1
 
1.0%
360877 1
 
1.0%
257618 1
 
1.0%
256323 1
 
1.0%
88528 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
17878 1
1.0%
25026 1
1.0%
35764 1
1.0%
41198 1
1.0%
48305 1
1.0%
48901 1
1.0%
49179 1
1.0%
50759 1
1.0%
51703 1
1.0%
53533 1
1.0%
ValueCountFrequency (%)
404674 1
1.0%
400326 1
1.0%
399476 1
1.0%
396849 1
1.0%
392267 1
1.0%
391835 1
1.0%
380887 1
1.0%
371950 1
1.0%
368146 1
1.0%
361569 1
1.0%

listing_url
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:43.263042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length35
Mean length34.66
Min length34

Characters and Unicode

Total characters3466
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/17878
2nd rowhttps://www.airbnb.com/rooms/25026
3rd rowhttps://www.airbnb.com/rooms/35764
4th rowhttps://www.airbnb.com/rooms/41198
5th rowhttps://www.airbnb.com/rooms/326205
ValueCountFrequency (%)
https://www.airbnb.com/rooms/17878 1
 
1.0%
https://www.airbnb.com/rooms/220705 1
 
1.0%
https://www.airbnb.com/rooms/35764 1
 
1.0%
https://www.airbnb.com/rooms/41198 1
 
1.0%
https://www.airbnb.com/rooms/326205 1
 
1.0%
https://www.airbnb.com/rooms/326575 1
 
1.0%
https://www.airbnb.com/rooms/216461 1
 
1.0%
https://www.airbnb.com/rooms/48305 1
 
1.0%
https://www.airbnb.com/rooms/216700 1
 
1.0%
https://www.airbnb.com/rooms/219250 1
 
1.0%
Other values (90) 90
90.0%
2024-04-28T15:05:43.629703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 400
 
11.5%
o 300
 
8.7%
w 300
 
8.7%
b 200
 
5.8%
m 200
 
5.8%
s 200
 
5.8%
. 200
 
5.8%
t 200
 
5.8%
r 200
 
5.8%
h 100
 
2.9%
Other values (16) 1166
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 400
 
11.5%
o 300
 
8.7%
w 300
 
8.7%
b 200
 
5.8%
m 200
 
5.8%
s 200
 
5.8%
. 200
 
5.8%
t 200
 
5.8%
r 200
 
5.8%
h 100
 
2.9%
Other values (16) 1166
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 400
 
11.5%
o 300
 
8.7%
w 300
 
8.7%
b 200
 
5.8%
m 200
 
5.8%
s 200
 
5.8%
. 200
 
5.8%
t 200
 
5.8%
r 200
 
5.8%
h 100
 
2.9%
Other values (16) 1166
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 400
 
11.5%
o 300
 
8.7%
w 300
 
8.7%
b 200
 
5.8%
m 200
 
5.8%
s 200
 
5.8%
. 200
 
5.8%
t 200
 
5.8%
r 200
 
5.8%
h 100
 
2.9%
Other values (16) 1166
33.6%

scrape_id
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20231226034138
100 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1400
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20231226034138
2nd row20231226034138
3rd row20231226034138
4th row20231226034138
5th row20231226034138

Common Values

ValueCountFrequency (%)
20231226034138 100
100.0%

Length

2024-04-28T15:05:43.768220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:43.859254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
20231226034138 100
100.0%

Most occurring characters

ValueCountFrequency (%)
2 400
28.6%
3 300
21.4%
0 200
14.3%
1 200
14.3%
6 100
 
7.1%
4 100
 
7.1%
8 100
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 400
28.6%
3 300
21.4%
0 200
14.3%
1 200
14.3%
6 100
 
7.1%
4 100
 
7.1%
8 100
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 400
28.6%
3 300
21.4%
0 200
14.3%
1 200
14.3%
6 100
 
7.1%
4 100
 
7.1%
8 100
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 400
28.6%
3 300
21.4%
0 200
14.3%
1 200
14.3%
6 100
 
7.1%
4 100
 
7.1%
8 100
 
7.1%

last_scraped
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-27
40 
2023-12-26
34 
2023-12-28
17 
2023-12-30
2023-12-29
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2023-12-27
2nd row2023-12-27
3rd row2023-12-27
4th row2023-12-27
5th row2023-12-27

Common Values

ValueCountFrequency (%)
2023-12-27 40
40.0%
2023-12-26 34
34.0%
2023-12-28 17
17.0%
2023-12-30 8
 
8.0%
2023-12-29 1
 
1.0%

Length

2024-04-28T15:05:43.951170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:44.043427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-27 40
40.0%
2023-12-26 34
34.0%
2023-12-28 17
17.0%
2023-12-30 8
 
8.0%
2023-12-29 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

source
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
city scrape
91 
previous scrape
 
9

Length

Max length15
Median length11
Mean length11.36
Min length11

Characters and Unicode

Total characters1136
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcity scrape
2nd rowcity scrape
3rd rowcity scrape
4th rowcity scrape
5th rowcity scrape

Common Values

ValueCountFrequency (%)
city scrape 91
91.0%
previous scrape 9
 
9.0%

Length

2024-04-28T15:05:44.168046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:44.262236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
scrape 100
50.0%
city 91
45.5%
previous 9
 
4.5%

Most occurring characters

ValueCountFrequency (%)
c 191
16.8%
s 109
9.6%
r 109
9.6%
p 109
9.6%
e 109
9.6%
i 100
8.8%
100
8.8%
a 100
8.8%
t 91
8.0%
y 91
8.0%
Other values (3) 27
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 191
16.8%
s 109
9.6%
r 109
9.6%
p 109
9.6%
e 109
9.6%
i 100
8.8%
100
8.8%
a 100
8.8%
t 91
8.0%
y 91
8.0%
Other values (3) 27
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 191
16.8%
s 109
9.6%
r 109
9.6%
p 109
9.6%
e 109
9.6%
i 100
8.8%
100
8.8%
a 100
8.8%
t 91
8.0%
y 91
8.0%
Other values (3) 27
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 191
16.8%
s 109
9.6%
r 109
9.6%
p 109
9.6%
e 109
9.6%
i 100
8.8%
100
8.8%
a 100
8.8%
t 91
8.0%
y 91
8.0%
Other values (3) 27
 
2.4%

name
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:44.393412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length83
Median length71
Mean length64.85
Min length42

Characters and Unicode

Total characters6485
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)94.0%

Sample

1st rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
2nd rowRental unit in Rio de Janeiro · ★4.72 · 1 bedroom · 1 bed · 1 bath
3rd rowLoft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 baths
4th rowRental unit in Rio de Janeiro · ★4.21 · 2 bedrooms · 1 bath
5th rowCondo in Rio de Janeiro · ★4.57 · 1 bedroom · 1 bed · 1 bath
ValueCountFrequency (%)
· 385
23.6%
1 163
 
10.0%
in 100
 
6.1%
rio 96
 
5.9%
de 90
 
5.5%
janeiro 90
 
5.5%
unit 72
 
4.4%
rental 72
 
4.4%
bedroom 62
 
3.8%
bath 61
 
3.7%
Other values (78) 442
27.1%
2024-04-28T15:05:44.678119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1535
23.7%
e 481
 
7.4%
o 417
 
6.4%
· 385
 
5.9%
i 372
 
5.7%
n 350
 
5.4%
d 309
 
4.8%
b 293
 
4.5%
a 287
 
4.4%
t 267
 
4.1%
Other values (35) 1789
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1535
23.7%
e 481
 
7.4%
o 417
 
6.4%
· 385
 
5.9%
i 372
 
5.7%
n 350
 
5.4%
d 309
 
4.8%
b 293
 
4.5%
a 287
 
4.4%
t 267
 
4.1%
Other values (35) 1789
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1535
23.7%
e 481
 
7.4%
o 417
 
6.4%
· 385
 
5.9%
i 372
 
5.7%
n 350
 
5.4%
d 309
 
4.8%
b 293
 
4.5%
a 287
 
4.4%
t 267
 
4.1%
Other values (35) 1789
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1535
23.7%
e 481
 
7.4%
o 417
 
6.4%
· 385
 
5.9%
i 372
 
5.7%
n 350
 
5.4%
d 309
 
4.8%
b 293
 
4.5%
a 287
 
4.4%
t 267
 
4.1%
Other values (35) 1789
27.6%

description
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

neighborhood_overview
Text

MISSING 

Distinct57
Distinct (%)100.0%
Missing43
Missing (%)43.0%
Memory size932.0 B
2024-04-28T15:05:45.092360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1000
Median length279
Mean length377.98246
Min length30

Characters and Unicode

Total characters21545
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st rowThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.
2nd rowCopacabana is a lively neighborhood and the apartment is located very close to an area in Copa full of bars, cafes and restaurants at Rua Bolivar and Domingos Ferreira. Copacabana never sleeps, there is always movement and it's a great mix of all kinds of people.
3rd rowOur guests will experience living with a local peole "Carioca" in a very friendly building with 24 hours a day security with all kind of stores, banks, transports, restaurants.
4th rowCome to stay in Baixo Copa, the more trendy and happy neighborhood of all Rio de Janeiro, in the heart of Copacabana, less than a half block from the beach. Restaurants, bars, grocery stores, theaters, banks, hotels and tourism agencies are in the neighborhood.
5th rowEnter Bossa Nova history by staying in the very street where real-life 'Girl From Ipanema' inspired Vinicius de Moraes to write the worldwide famous song, "each day when she walks to the sea".<br /><br />Located seconds from Ipanema Beach's best spot Posto 9, on the first beach block in the heart of Ipanema’s very best neighbourhood, enjoy staying in this elegant and modern hideaway in a peaceful residential street.<br /><br />Ipanema and Leblon are by far the safest locations in Rio, with popular restaurants, cafes and boutique stores walking distance to the apartment.
ValueCountFrequency (%)
the 175
 
4.9%
and 113
 
3.2%
to 77
 
2.2%
a 75
 
2.1%
of 68
 
1.9%
de 60
 
1.7%
in 56
 
1.6%
is 55
 
1.6%
e 53
 
1.5%
br 41
 
1.2%
Other values (1271) 2773
78.2%
2024-04-28T15:05:45.710877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3528
16.4%
a 1866
 
8.7%
e 1828
 
8.5%
o 1397
 
6.5%
t 1293
 
6.0%
r 1263
 
5.9%
s 1116
 
5.2%
i 1111
 
5.2%
n 1010
 
4.7%
d 616
 
2.9%
Other values (88) 6517
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3528
16.4%
a 1866
 
8.7%
e 1828
 
8.5%
o 1397
 
6.5%
t 1293
 
6.0%
r 1263
 
5.9%
s 1116
 
5.2%
i 1111
 
5.2%
n 1010
 
4.7%
d 616
 
2.9%
Other values (88) 6517
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3528
16.4%
a 1866
 
8.7%
e 1828
 
8.5%
o 1397
 
6.5%
t 1293
 
6.0%
r 1263
 
5.9%
s 1116
 
5.2%
i 1111
 
5.2%
n 1010
 
4.7%
d 616
 
2.9%
Other values (88) 6517
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3528
16.4%
a 1866
 
8.7%
e 1828
 
8.5%
o 1397
 
6.5%
t 1293
 
6.0%
r 1263
 
5.9%
s 1116
 
5.2%
i 1111
 
5.2%
n 1010
 
4.7%
d 616
 
2.9%
Other values (88) 6517
30.2%

picture_url
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:46.016177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length110
Median length109
Mean length71.84
Min length61

Characters and Unicode

Total characters7184
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowhttps://a0.muscache.com/pictures/65320518/30698f38_original.jpg
2nd rowhttps://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg
3rd rowhttps://a0.muscache.com/pictures/23782972/1d3e55b0_original.jpg
4th rowhttps://a0.muscache.com/pictures/3576716/2d6a9301_original.jpg
5th rowhttps://a0.muscache.com/pictures/c550151d-910c-40c6-96a8-d2a8bd770361.jpg
ValueCountFrequency (%)
https://a0.muscache.com/pictures/65320518/30698f38_original.jpg 1
 
1.0%
https://a0.muscache.com/pictures/d0fc79d7-bb33-47fa-bfea-f4ca92956b79.jpg 1
 
1.0%
https://a0.muscache.com/pictures/23782972/1d3e55b0_original.jpg 1
 
1.0%
https://a0.muscache.com/pictures/3576716/2d6a9301_original.jpg 1
 
1.0%
https://a0.muscache.com/pictures/c550151d-910c-40c6-96a8-d2a8bd770361.jpg 1
 
1.0%
https://a0.muscache.com/pictures/4cffcbcf-16c2-4624-afee-29a7ffe20698.jpg 1
 
1.0%
https://a0.muscache.com/pictures/2628485/1ed768bb_original.jpg 1
 
1.0%
https://a0.muscache.com/pictures/miso/hosting-48305/original/cce14bf9-c5b6-44c6-a89f-61323975afdb.jpeg 1
 
1.0%
https://a0.muscache.com/pictures/6162310/be07750f_original.jpg 1
 
1.0%
https://a0.muscache.com/pictures/60226390/d079690d_original.jpg 1
 
1.0%
Other values (90) 90
90.0%
2024-04-28T15:05:46.470919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 510
 
7.1%
/ 501
 
7.0%
a 390
 
5.4%
e 336
 
4.7%
s 328
 
4.6%
t 320
 
4.5%
p 310
 
4.3%
. 300
 
4.2%
i 272
 
3.8%
0 262
 
3.6%
Other values (26) 3655
50.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 510
 
7.1%
/ 501
 
7.0%
a 390
 
5.4%
e 336
 
4.7%
s 328
 
4.6%
t 320
 
4.5%
p 310
 
4.3%
. 300
 
4.2%
i 272
 
3.8%
0 262
 
3.6%
Other values (26) 3655
50.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 510
 
7.1%
/ 501
 
7.0%
a 390
 
5.4%
e 336
 
4.7%
s 328
 
4.6%
t 320
 
4.5%
p 310
 
4.3%
. 300
 
4.2%
i 272
 
3.8%
0 262
 
3.6%
Other values (26) 3655
50.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 510
 
7.1%
/ 501
 
7.0%
a 390
 
5.4%
e 336
 
4.7%
s 328
 
4.6%
t 320
 
4.5%
p 310
 
4.3%
. 300
 
4.2%
i 272
 
3.8%
0 262
 
3.6%
Other values (26) 3655
50.9%

host_id
Real number (ℝ)

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean979632.26
Minimum68997
Maximum7506316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:46.616753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum68997
5-th percentile109643.9
Q1406904.5
median798441
Q31418424.8
95-th percentile1962093.5
Maximum7506316
Range7437319
Interquartile range (IQR)1011520.2

Descriptive statistics

Standard deviation907412.4
Coefficient of variation (CV)0.9262786
Kurtosis26.054933
Mean979632.26
Median Absolute Deviation (MAD)516924.5
Skewness3.8534002
Sum97963226
Variance8.2339727 × 1011
MonotonicityNot monotonic
2024-04-28T15:05:46.753633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
474221 3
 
3.0%
406989 3
 
3.0%
829630 2
 
2.0%
1603206 2
 
2.0%
1858602 2
 
2.0%
1298591 2
 
2.0%
70933 2
 
2.0%
235496 2
 
2.0%
1172595 2
 
2.0%
1207700 2
 
2.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
68997 1
1.0%
70933 2
2.0%
93005 1
1.0%
102840 1
1.0%
110002 1
1.0%
132230 1
1.0%
153691 1
1.0%
178975 1
1.0%
222884 1
1.0%
224192 1
1.0%
ValueCountFrequency (%)
7506316 1
1.0%
2537111 1
1.0%
1998370 1
1.0%
1994382 1
1.0%
1982737 1
1.0%
1961007 1
1.0%
1910409 1
1.0%
1858602 2
2.0%
1824379 1
1.0%
1809627 1
1.0%
Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:46.994334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length41
Median length40
Mean length40.43
Min length39

Characters and Unicode

Total characters4043
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)78.0%

Sample

1st rowhttps://www.airbnb.com/users/show/68997
2nd rowhttps://www.airbnb.com/users/show/102840
3rd rowhttps://www.airbnb.com/users/show/153691
4th rowhttps://www.airbnb.com/users/show/178975
5th rowhttps://www.airbnb.com/users/show/1603206
ValueCountFrequency (%)
https://www.airbnb.com/users/show/474221 3
 
3.0%
https://www.airbnb.com/users/show/406989 3
 
3.0%
https://www.airbnb.com/users/show/70933 2
 
2.0%
https://www.airbnb.com/users/show/1172595 2
 
2.0%
https://www.airbnb.com/users/show/235496 2
 
2.0%
https://www.airbnb.com/users/show/1207700 2
 
2.0%
https://www.airbnb.com/users/show/1298591 2
 
2.0%
https://www.airbnb.com/users/show/1858602 2
 
2.0%
https://www.airbnb.com/users/show/1603206 2
 
2.0%
https://www.airbnb.com/users/show/829630 2
 
2.0%
Other values (78) 78
78.0%
2024-04-28T15:05:47.391882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 500
 
12.4%
s 400
 
9.9%
w 400
 
9.9%
h 200
 
4.9%
r 200
 
4.9%
t 200
 
4.9%
b 200
 
4.9%
o 200
 
4.9%
. 200
 
4.9%
1 109
 
2.7%
Other values (18) 1434
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 500
 
12.4%
s 400
 
9.9%
w 400
 
9.9%
h 200
 
4.9%
r 200
 
4.9%
t 200
 
4.9%
b 200
 
4.9%
o 200
 
4.9%
. 200
 
4.9%
1 109
 
2.7%
Other values (18) 1434
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 500
 
12.4%
s 400
 
9.9%
w 400
 
9.9%
h 200
 
4.9%
r 200
 
4.9%
t 200
 
4.9%
b 200
 
4.9%
o 200
 
4.9%
. 200
 
4.9%
1 109
 
2.7%
Other values (18) 1434
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 500
 
12.4%
s 400
 
9.9%
w 400
 
9.9%
h 200
 
4.9%
r 200
 
4.9%
t 200
 
4.9%
b 200
 
4.9%
o 200
 
4.9%
. 200
 
4.9%
1 109
 
2.7%
Other values (18) 1434
35.5%
Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:47.707360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length24
Median length16
Mean length7.46
Min length3

Characters and Unicode

Total characters746
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)70.0%

Sample

1st rowMatthias
2nd rowViviane
3rd rowPatricia Miranda & Paulo
4th rowNicky
5th rowBob
ValueCountFrequency (%)
maria 5
 
3.8%
5
 
3.8%
casa 4
 
3.0%
silvia 3
 
2.3%
bob 3
 
2.3%
48 3
 
2.3%
guesthouse 3
 
2.3%
ricardo 3
 
2.3%
june 3
 
2.3%
da 2
 
1.5%
Other values (89) 98
74.2%
2024-04-28T15:05:48.136543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 129
17.3%
i 71
 
9.5%
r 51
 
6.8%
e 51
 
6.8%
n 40
 
5.4%
o 40
 
5.4%
l 35
 
4.7%
33
 
4.4%
s 29
 
3.9%
u 20
 
2.7%
Other values (46) 247
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 129
17.3%
i 71
 
9.5%
r 51
 
6.8%
e 51
 
6.8%
n 40
 
5.4%
o 40
 
5.4%
l 35
 
4.7%
33
 
4.4%
s 29
 
3.9%
u 20
 
2.7%
Other values (46) 247
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 129
17.3%
i 71
 
9.5%
r 51
 
6.8%
e 51
 
6.8%
n 40
 
5.4%
o 40
 
5.4%
l 35
 
4.7%
33
 
4.4%
s 29
 
3.9%
u 20
 
2.7%
Other values (46) 247
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 129
17.3%
i 71
 
9.5%
r 51
 
6.8%
e 51
 
6.8%
n 40
 
5.4%
o 40
 
5.4%
l 35
 
4.7%
33
 
4.4%
s 29
 
3.9%
u 20
 
2.7%
Other values (46) 247
33.1%
Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2010-01-08 00:00:00
Maximum2013-07-15 00:00:00
2024-04-28T15:05:48.316409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:48.486364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

host_location
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)12.4%
Missing3
Missing (%)3.0%
Memory size932.0 B
Rio de Janeiro, Brazil
81 
Brazil
 
5
State of Rio de Janeiro, Brazil
 
2
Rio, Brazil
 
1
Mesa, AZ
 
1
Other values (7)
 
7

Length

Max length31
Median length22
Mean length20.639175
Min length6

Characters and Unicode

Total characters2002
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)9.3%

Sample

1st rowRio de Janeiro, Brazil
2nd rowRio de Janeiro, Brazil
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowRio de Janeiro, Brazil

Common Values

ValueCountFrequency (%)
Rio de Janeiro, Brazil 81
81.0%
Brazil 5
 
5.0%
State of Rio de Janeiro, Brazil 2
 
2.0%
Rio, Brazil 1
 
1.0%
Mesa, AZ 1
 
1.0%
San Diego, CA 1
 
1.0%
Florianópolis, Brazil 1
 
1.0%
Sao Paulo, Brazil 1
 
1.0%
Berlin, Germany 1
 
1.0%
Miami Beach, FL 1
 
1.0%
Other values (2) 2
 
2.0%
(Missing) 3
 
3.0%

Length

2024-04-28T15:05:48.653927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brazil 91
25.1%
rio 84
23.2%
janeiro 83
22.9%
de 83
22.9%
state 2
 
0.6%
of 2
 
0.6%
berlin 1
 
0.3%
zug 1
 
0.3%
spain 1
 
0.3%
palma 1
 
0.3%
Other values (13) 13
 
3.6%

Most occurring characters

ValueCountFrequency (%)
i 266
13.3%
265
13.2%
a 188
9.4%
r 178
8.9%
o 174
8.7%
e 174
8.7%
l 97
 
4.8%
B 93
 
4.6%
z 92
 
4.6%
, 92
 
4.6%
Other values (26) 383
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 266
13.3%
265
13.2%
a 188
9.4%
r 178
8.9%
o 174
8.7%
e 174
8.7%
l 97
 
4.8%
B 93
 
4.6%
z 92
 
4.6%
, 92
 
4.6%
Other values (26) 383
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 266
13.3%
265
13.2%
a 188
9.4%
r 178
8.9%
o 174
8.7%
e 174
8.7%
l 97
 
4.8%
B 93
 
4.6%
z 92
 
4.6%
, 92
 
4.6%
Other values (26) 383
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 266
13.3%
265
13.2%
a 188
9.4%
r 178
8.9%
o 174
8.7%
e 174
8.7%
l 97
 
4.8%
B 93
 
4.6%
z 92
 
4.6%
, 92
 
4.6%
Other values (26) 383
19.1%

host_about
Text

MISSING 

Distinct84
Distinct (%)87.5%
Missing4
Missing (%)4.0%
Memory size932.0 B
2024-04-28T15:05:49.085155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3837
Median length405
Mean length492.80208
Min length63

Characters and Unicode

Total characters47309
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)77.1%

Sample

1st rowI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.
2nd rowHi guys, Viviane is a commercial photographer, an avid world traveler, (a former photographer for Airbnb) and an Airbnb superhost. And a free lance photographer for other wonderful clients. She loves life and meeting people. We work together in providing the best accommodation to people and we are firm believers of enjoying the moment as a prime attitude towards life!
3rd rowHello, We are Patricia Miranda and Paulo. We are a couple who love to meet new people, new cultures, we both are very easy going persons, We are retired after working for several years in tourism and an international airline company. We also used do host in our own residence International students from all over the world. We are gay friendly and everybody is welcome! !
4th rowHi fellow Airbnb-ers, Mr Francis , Nixx, Nicky ,Nick call me what you like Originally from England but now in South America I love This crazy thing called life. I enjoy meeting interesting multi dimensional people from any country, class or colour ., I don't understand negative or petty people! because I believe life is just too short! I'm not the greatest tour guide, but it would be my pleasure to help you where I can. I administrate a few places on here, so feel free to take a browse of my listings. Please remember that some of these places may be owners homes and not hotels, So don't expect the hilton treatment as I like to keep it sweet & simple. Let me know also if the apt is different from what was listed and I will do my best to Rectify the situation. Remember even though we are here to do a lil Bizniz the most important thing is to enjoy Rio de Janeiro in all it's splendour ! Rio is always an Experience! My favourite travel hotspots outside of Brazil - Colombia or Caribbean Here is a story for you There was a married American guy called Lance who was working abroad as a computer systems analyst to make ends meet.He traveled a lot for his job And had been doing so for the last 37 years. Last year whilst working in Thailand He picked up a young sexy wife 23 years old she was and a real beauty! It was really difficult for him to leave her at home, but the bills needed to be paid and besides his wife was high maintenance. In a particularly difficult month he received a message from his Desirable Wife who was taking care of his home" I signed up for a new service Airbnb Where you can rent space in your house,make good money and I buy new Shoes" Having a beautiful home (and not to mention photo), it wasn't long before she received A guest, actually a handsome pilot from Greece, Adonis was his name. After 5 days Lance could hardly make contact with his wife but unexpectedly received a message from his Airbnb guest. : Hi Sir, Thank you, I enjoyed using your home & everything your city had to offer, I hope you won't be mad but I was using your wife...I was using day and night ... I explored immensely, I was using when you are not present at home... In fact I was using more than You are using..... I confess this because now I feel very much guilt... And is not acceptable under the eyes of God Hope You will accept my sincere apologies". ... Lance distraught with this revelation , jumped On the next plane to his city, , entered his home tied & taped up his wife., and made her watch while he cut Up and burnt all her expensive clothes and shoes, put her in front of the mirror And proceeded to shear off her hair Few minutes later he received another message : Sorry sir, spelling mistake ... wifi not wife. Lol!
5th rowI'm originally from the US, but moved to this Great City of Rio de Janeiro.
ValueCountFrequency (%)
and 240
 
2.9%
e 185
 
2.3%
the 183
 
2.2%
a 182
 
2.2%
de 180
 
2.2%
to 179
 
2.2%
i 148
 
1.8%
in 135
 
1.6%
110
 
1.3%
rio 110
 
1.3%
Other values (2166) 6547
79.9%
2024-04-28T15:05:49.714040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8343
17.6%
e 4312
 
9.1%
a 3847
 
8.1%
o 3214
 
6.8%
i 2613
 
5.5%
r 2313
 
4.9%
n 2312
 
4.9%
s 2304
 
4.9%
t 2303
 
4.9%
d 1482
 
3.1%
Other values (101) 14266
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8343
17.6%
e 4312
 
9.1%
a 3847
 
8.1%
o 3214
 
6.8%
i 2613
 
5.5%
r 2313
 
4.9%
n 2312
 
4.9%
s 2304
 
4.9%
t 2303
 
4.9%
d 1482
 
3.1%
Other values (101) 14266
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8343
17.6%
e 4312
 
9.1%
a 3847
 
8.1%
o 3214
 
6.8%
i 2613
 
5.5%
r 2313
 
4.9%
n 2312
 
4.9%
s 2304
 
4.9%
t 2303
 
4.9%
d 1482
 
3.1%
Other values (101) 14266
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8343
17.6%
e 4312
 
9.1%
a 3847
 
8.1%
o 3214
 
6.8%
i 2613
 
5.5%
r 2313
 
4.9%
n 2312
 
4.9%
s 2304
 
4.9%
t 2303
 
4.9%
d 1482
 
3.1%
Other values (101) 14266
30.2%

host_response_time
Categorical

MISSING 

Distinct4
Distinct (%)4.6%
Missing13
Missing (%)13.0%
Memory size932.0 B
within an hour
47 
within a few hours
29 
within a day
a few days or more

Length

Max length18
Median length14
Mean length15.425287
Min length12

Characters and Unicode

Total characters1342
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin an hour
2nd rowwithin an hour
3rd rowwithin an hour
4th rowwithin an hour
5th rowwithin a few hours

Common Values

ValueCountFrequency (%)
within an hour 47
47.0%
within a few hours 29
29.0%
within a day 6
 
6.0%
a few days or more 5
 
5.0%
(Missing) 13
 
13.0%

Length

2024-04-28T15:05:49.856947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:49.958654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
within 82
27.3%
an 47
15.7%
hour 47
15.7%
a 40
13.3%
few 34
11.3%
hours 29
 
9.7%
day 6
 
2.0%
days 5
 
1.7%
or 5
 
1.7%
more 5
 
1.7%

Most occurring characters

ValueCountFrequency (%)
213
15.9%
i 164
12.2%
h 158
11.8%
n 129
9.6%
w 116
8.6%
a 98
7.3%
o 86
6.4%
r 86
6.4%
t 82
 
6.1%
u 76
 
5.7%
Other values (6) 134
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
213
15.9%
i 164
12.2%
h 158
11.8%
n 129
9.6%
w 116
8.6%
a 98
7.3%
o 86
6.4%
r 86
6.4%
t 82
 
6.1%
u 76
 
5.7%
Other values (6) 134
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
213
15.9%
i 164
12.2%
h 158
11.8%
n 129
9.6%
w 116
8.6%
a 98
7.3%
o 86
6.4%
r 86
6.4%
t 82
 
6.1%
u 76
 
5.7%
Other values (6) 134
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
213
15.9%
i 164
12.2%
h 158
11.8%
n 129
9.6%
w 116
8.6%
a 98
7.3%
o 86
6.4%
r 86
6.4%
t 82
 
6.1%
u 76
 
5.7%
Other values (6) 134
10.0%

host_response_rate
Categorical

IMBALANCE  MISSING 

Distinct9
Distinct (%)10.3%
Missing13
Missing (%)13.0%
Memory size932.0 B
100%
66 
90%
0%
 
5
50%
 
2
96%
 
1
Other values (4)
 
4

Length

Max length4
Median length4
Mean length3.7011494
Min length2

Characters and Unicode

Total characters322
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.7%

Sample

1st row100%
2nd row100%
3rd row100%
4th row90%
5th row100%

Common Values

ValueCountFrequency (%)
100% 66
66.0%
90% 9
 
9.0%
0% 5
 
5.0%
50% 2
 
2.0%
96% 1
 
1.0%
70% 1
 
1.0%
63% 1
 
1.0%
80% 1
 
1.0%
99% 1
 
1.0%
(Missing) 13
 
13.0%

Length

2024-04-28T15:05:50.084911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:50.210663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
100 66
75.9%
90 9
 
10.3%
0 5
 
5.7%
50 2
 
2.3%
96 1
 
1.1%
70 1
 
1.1%
63 1
 
1.1%
80 1
 
1.1%
99 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 150
46.6%
% 87
27.0%
1 66
20.5%
9 12
 
3.7%
5 2
 
0.6%
6 2
 
0.6%
7 1
 
0.3%
3 1
 
0.3%
8 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 150
46.6%
% 87
27.0%
1 66
20.5%
9 12
 
3.7%
5 2
 
0.6%
6 2
 
0.6%
7 1
 
0.3%
3 1
 
0.3%
8 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 150
46.6%
% 87
27.0%
1 66
20.5%
9 12
 
3.7%
5 2
 
0.6%
6 2
 
0.6%
7 1
 
0.3%
3 1
 
0.3%
8 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 150
46.6%
% 87
27.0%
1 66
20.5%
9 12
 
3.7%
5 2
 
0.6%
6 2
 
0.6%
7 1
 
0.3%
3 1
 
0.3%
8 1
 
0.3%

host_acceptance_rate
Categorical

MISSING 

Distinct32
Distinct (%)38.1%
Missing16
Missing (%)16.0%
Memory size932.0 B
100%
19 
93%
0%
96%
 
3
88%
 
3
Other values (27)
48 

Length

Max length4
Median length3
Mean length3.1428571
Min length2

Characters and Unicode

Total characters264
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)16.7%

Sample

1st row96%
2nd row80%
3rd row98%
4th row89%
5th row93%

Common Values

ValueCountFrequency (%)
100% 19
19.0%
93% 6
 
6.0%
0% 5
 
5.0%
96% 3
 
3.0%
88% 3
 
3.0%
83% 3
 
3.0%
92% 3
 
3.0%
77% 3
 
3.0%
95% 3
 
3.0%
86% 3
 
3.0%
Other values (22) 33
33.0%
(Missing) 16
16.0%

Length

2024-04-28T15:05:50.347442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 19
22.6%
93 6
 
7.1%
0 5
 
6.0%
96 3
 
3.6%
88 3
 
3.6%
83 3
 
3.6%
92 3
 
3.6%
77 3
 
3.6%
95 3
 
3.6%
86 3
 
3.6%
Other values (22) 33
39.3%

Most occurring characters

ValueCountFrequency (%)
% 84
31.8%
0 46
17.4%
9 32
 
12.1%
8 25
 
9.5%
1 22
 
8.3%
7 15
 
5.7%
3 10
 
3.8%
6 10
 
3.8%
5 9
 
3.4%
4 6
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 84
31.8%
0 46
17.4%
9 32
 
12.1%
8 25
 
9.5%
1 22
 
8.3%
7 15
 
5.7%
3 10
 
3.8%
6 10
 
3.8%
5 9
 
3.4%
4 6
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 84
31.8%
0 46
17.4%
9 32
 
12.1%
8 25
 
9.5%
1 22
 
8.3%
7 15
 
5.7%
3 10
 
3.8%
6 10
 
3.8%
5 9
 
3.4%
4 6
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 84
31.8%
0 46
17.4%
9 32
 
12.1%
8 25
 
9.5%
1 22
 
8.3%
7 15
 
5.7%
3 10
 
3.8%
6 10
 
3.8%
5 9
 
3.4%
4 6
 
2.3%
Distinct2
Distinct (%)2.0%
Missing1
Missing (%)1.0%
Memory size332.0 B
False
56 
True
43 
(Missing)
 
1
ValueCountFrequency (%)
False 56
56.0%
True 43
43.0%
(Missing) 1
 
1.0%
2024-04-28T15:05:50.445146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:50.655581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length129
Median length128.5
Mean length104.65
Min length99

Characters and Unicode

Total characters10465
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)78.0%

Sample

1st rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_small
2nd rowhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_small
3rd rowhttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_small
4th rowhttps://a0.muscache.com/im/users/178975/profile_pic/1384245754/original.jpg?aki_policy=profile_small
5th rowhttps://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_small
ValueCountFrequency (%)
https://a0.muscache.com/im/pictures/user/1b75500d-9a03-4d7e-87b7-e578a0773444.jpg?aki_policy=profile_small 3
 
3.0%
https://a0.muscache.com/im/pictures/user/31fc9c9f-26b4-44d6-bdaa-9f9d5ded347e.jpg?aki_policy=profile_small 3
 
3.0%
https://a0.muscache.com/im/pictures/user/c2d77835-fab6-45f9-a3ef-ef570dda44a5.jpg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/pictures/user/45ee1619-302f-47cc-8c04-41e8b85278dc.jpg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/pictures/user/user-235496/original/c89b1709-3da6-4618-8b5a-996139275263.jpeg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/users/1298591/profile_pic/1421192290/original.jpg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/users/1858602/profile_pic/1330914215/original.jpg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_small 2
 
2.0%
https://a0.muscache.com/im/users/829630/profile_pic/1326368431/original.jpg?aki_policy=profile_small 2
 
2.0%
Other values (78) 78
78.0%
2024-04-28T15:05:51.010515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 700
 
6.7%
i 645
 
6.2%
c 597
 
5.7%
a 579
 
5.5%
p 545
 
5.2%
e 507
 
4.8%
s 505
 
4.8%
l 495
 
4.7%
m 400
 
3.8%
o 395
 
3.8%
Other values (29) 5097
48.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 700
 
6.7%
i 645
 
6.2%
c 597
 
5.7%
a 579
 
5.5%
p 545
 
5.2%
e 507
 
4.8%
s 505
 
4.8%
l 495
 
4.7%
m 400
 
3.8%
o 395
 
3.8%
Other values (29) 5097
48.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 700
 
6.7%
i 645
 
6.2%
c 597
 
5.7%
a 579
 
5.5%
p 545
 
5.2%
e 507
 
4.8%
s 505
 
4.8%
l 495
 
4.7%
m 400
 
3.8%
o 395
 
3.8%
Other values (29) 5097
48.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 700
 
6.7%
i 645
 
6.2%
c 597
 
5.7%
a 579
 
5.5%
p 545
 
5.2%
e 507
 
4.8%
s 505
 
4.8%
l 495
 
4.7%
m 400
 
3.8%
o 395
 
3.8%
Other values (29) 5097
48.7%
Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-28T15:05:51.255153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length132
Median length131.5
Mean length107.65
Min length102

Characters and Unicode

Total characters10765
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)78.0%

Sample

1st rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_medium
2nd rowhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_x_medium
3rd rowhttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_x_medium
4th rowhttps://a0.muscache.com/im/users/178975/profile_pic/1384245754/original.jpg?aki_policy=profile_x_medium
5th rowhttps://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_x_medium
ValueCountFrequency (%)
https://a0.muscache.com/im/pictures/user/1b75500d-9a03-4d7e-87b7-e578a0773444.jpg?aki_policy=profile_x_medium 3
 
3.0%
https://a0.muscache.com/im/pictures/user/31fc9c9f-26b4-44d6-bdaa-9f9d5ded347e.jpg?aki_policy=profile_x_medium 3
 
3.0%
https://a0.muscache.com/im/pictures/user/c2d77835-fab6-45f9-a3ef-ef570dda44a5.jpg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/pictures/user/45ee1619-302f-47cc-8c04-41e8b85278dc.jpg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/pictures/user/user-235496/original/c89b1709-3da6-4618-8b5a-996139275263.jpeg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/users/1298591/profile_pic/1421192290/original.jpg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/users/1858602/profile_pic/1330914215/original.jpg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_x_medium 2
 
2.0%
https://a0.muscache.com/im/users/829630/profile_pic/1326368431/original.jpg?aki_policy=profile_x_medium 2
 
2.0%
Other values (78) 78
78.0%
2024-04-28T15:05:51.599841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 745
 
6.9%
/ 700
 
6.5%
e 607
 
5.6%
c 597
 
5.5%
p 545
 
5.1%
m 500
 
4.6%
a 479
 
4.4%
s 405
 
3.8%
o 395
 
3.7%
u 355
 
3.3%
Other values (30) 5437
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 745
 
6.9%
/ 700
 
6.5%
e 607
 
5.6%
c 597
 
5.5%
p 545
 
5.1%
m 500
 
4.6%
a 479
 
4.4%
s 405
 
3.8%
o 395
 
3.7%
u 355
 
3.3%
Other values (30) 5437
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 745
 
6.9%
/ 700
 
6.5%
e 607
 
5.6%
c 597
 
5.5%
p 545
 
5.1%
m 500
 
4.6%
a 479
 
4.4%
s 405
 
3.8%
o 395
 
3.7%
u 355
 
3.3%
Other values (30) 5437
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 745
 
6.9%
/ 700
 
6.5%
e 607
 
5.6%
c 597
 
5.5%
p 545
 
5.1%
m 500
 
4.6%
a 479
 
4.4%
s 405
 
3.8%
o 395
 
3.7%
u 355
 
3.3%
Other values (30) 5437
50.5%
Distinct17
Distinct (%)17.2%
Missing1
Missing (%)1.0%
Memory size932.0 B
Copacabana
44 
Santa Teresa
10 
Ipanema
Leblon
Barra da Tijuca
Other values (12)
23 

Length

Max length24
Median length15
Mean length9.5656566
Min length3

Characters and Unicode

Total characters947
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.1%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana

Common Values

ValueCountFrequency (%)
Copacabana 44
44.0%
Santa Teresa 10
 
10.0%
Ipanema 9
 
9.0%
Leblon 7
 
7.0%
Barra da Tijuca 6
 
6.0%
Flamengo 4
 
4.0%
Botafogo 3
 
3.0%
Lapa 3
 
3.0%
Jardin Botânico 2
 
2.0%
Vidigal 2
 
2.0%
Other values (7) 9
 
9.0%

Length

2024-04-28T15:05:51.739496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copacabana 44
34.6%
teresa 10
 
7.9%
santa 10
 
7.9%
ipanema 9
 
7.1%
leblon 7
 
5.5%
da 7
 
5.5%
tijuca 7
 
5.5%
barra 6
 
4.7%
flamengo 4
 
3.1%
botafogo 3
 
2.4%
Other values (13) 20
15.7%

Most occurring characters

ValueCountFrequency (%)
a 278
29.4%
n 83
 
8.8%
o 72
 
7.6%
p 56
 
5.9%
c 54
 
5.7%
b 51
 
5.4%
e 50
 
5.3%
C 44
 
4.6%
r 30
 
3.2%
28
 
3.0%
Other values (25) 201
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 278
29.4%
n 83
 
8.8%
o 72
 
7.6%
p 56
 
5.9%
c 54
 
5.7%
b 51
 
5.4%
e 50
 
5.3%
C 44
 
4.6%
r 30
 
3.2%
28
 
3.0%
Other values (25) 201
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 278
29.4%
n 83
 
8.8%
o 72
 
7.6%
p 56
 
5.9%
c 54
 
5.7%
b 51
 
5.4%
e 50
 
5.3%
C 44
 
4.6%
r 30
 
3.2%
28
 
3.0%
Other values (25) 201
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 278
29.4%
n 83
 
8.8%
o 72
 
7.6%
p 56
 
5.9%
c 54
 
5.7%
b 51
 
5.4%
e 50
 
5.3%
C 44
 
4.6%
r 30
 
3.2%
28
 
3.0%
Other values (25) 201
21.2%

host_listings_count
Real number (ℝ)

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.93
Minimum1
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:51.846174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile10.1
Maximum144
Range143
Interquartile range (IQR)4

Descriptive statistics

Standard deviation14.372571
Coefficient of variation (CV)2.9153289
Kurtosis90.998365
Mean4.93
Median Absolute Deviation (MAD)2
Skewness9.3431675
Sum493
Variance206.57081
MonotonicityNot monotonic
2024-04-28T15:05:51.949435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 33
33.0%
2 16
16.0%
4 11
 
11.0%
5 10
 
10.0%
3 9
 
9.0%
6 9
 
9.0%
7 4
 
4.0%
12 3
 
3.0%
8 2
 
2.0%
19 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
1 33
33.0%
2 16
16.0%
3 9
 
9.0%
4 11
 
11.0%
5 10
 
10.0%
6 9
 
9.0%
7 4
 
4.0%
8 2
 
2.0%
10 1
 
1.0%
12 3
 
3.0%
ValueCountFrequency (%)
144 1
 
1.0%
19 1
 
1.0%
12 3
 
3.0%
10 1
 
1.0%
8 2
 
2.0%
7 4
 
4.0%
6 9
9.0%
5 10
10.0%
4 11
11.0%
3 9
9.0%

host_total_listings_count
Real number (ℝ)

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.09
Minimum1
Maximum429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:52.054157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median5
Q38
95-th percentile27.05
Maximum429
Range428
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation42.776869
Coefficient of variation (CV)3.857247
Kurtosis94.675462
Mean11.09
Median Absolute Deviation (MAD)3
Skewness9.6114809
Sum1109
Variance1829.8605
MonotonicityNot monotonic
2024-04-28T15:05:52.189191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 13
13.0%
1 13
13.0%
2 12
12.0%
6 9
9.0%
4 8
8.0%
5 8
8.0%
8 8
8.0%
10 7
7.0%
7 6
6.0%
15 3
 
3.0%
Other values (11) 13
13.0%
ValueCountFrequency (%)
1 13
13.0%
2 12
12.0%
3 13
13.0%
4 8
8.0%
5 8
8.0%
6 9
9.0%
7 6
6.0%
8 8
8.0%
9 1
 
1.0%
10 7
7.0%
ValueCountFrequency (%)
429 1
 
1.0%
33 2
2.0%
31 1
 
1.0%
28 1
 
1.0%
27 1
 
1.0%
23 1
 
1.0%
21 1
 
1.0%
17 1
 
1.0%
15 3
3.0%
13 1
 
1.0%

host_verifications
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
['email', 'phone']
86 
['email', 'phone', 'work_email']
10 
['phone']
 
3
['email']
 
1

Length

Max length32
Median length18
Mean length19.04
Min length9

Characters and Unicode

Total characters1904
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row['email', 'phone']
2nd row['email', 'phone']
3rd row['email', 'phone']
4th row['email', 'phone']
5th row['email', 'phone']

Common Values

ValueCountFrequency (%)
['email', 'phone'] 86
86.0%
['email', 'phone', 'work_email'] 10
 
10.0%
['phone'] 3
 
3.0%
['email'] 1
 
1.0%

Length

2024-04-28T15:05:52.305649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:52.415846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
phone 99
48.1%
email 97
47.1%
work_email 10
 
4.9%

Most occurring characters

ValueCountFrequency (%)
' 412
21.6%
e 206
10.8%
o 109
 
5.7%
m 107
 
5.6%
a 107
 
5.6%
i 107
 
5.6%
l 107
 
5.6%
, 106
 
5.6%
106
 
5.6%
[ 100
 
5.3%
Other values (8) 437
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 412
21.6%
e 206
10.8%
o 109
 
5.7%
m 107
 
5.6%
a 107
 
5.6%
i 107
 
5.6%
l 107
 
5.6%
, 106
 
5.6%
106
 
5.6%
[ 100
 
5.3%
Other values (8) 437
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 412
21.6%
e 206
10.8%
o 109
 
5.7%
m 107
 
5.6%
a 107
 
5.6%
i 107
 
5.6%
l 107
 
5.6%
, 106
 
5.6%
106
 
5.6%
[ 100
 
5.3%
Other values (8) 437
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 412
21.6%
e 206
10.8%
o 109
 
5.7%
m 107
 
5.6%
a 107
 
5.6%
i 107
 
5.6%
l 107
 
5.6%
, 106
 
5.6%
106
 
5.6%
[ 100
 
5.3%
Other values (8) 437
23.0%

host_has_profile_pic
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
100 
ValueCountFrequency (%)
True 100
100.0%
2024-04-28T15:05:52.515538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
83 
False
17 
ValueCountFrequency (%)
True 83
83.0%
False 17
 
17.0%
2024-04-28T15:05:52.589691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

neighbourhood
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)14.0%
Missing43
Missing (%)43.0%
Memory size932.0 B
Rio de Janeiro, Brazil
46 
Rio, Rio de Janeiro, Brazil
Ipanema, Rio de Janeiro, Brazil
 
1
Rio de Janeiro, Rj, Brazil
 
1
Joatinga, Rio de Janeiro, Brazil
 
1
Other values (3)
 
3

Length

Max length39
Median length22
Mean length23.684211
Min length22

Characters and Unicode

Total characters1350
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)10.5%

Sample

1st rowRio de Janeiro, Brazil
2nd rowRio de Janeiro, Brazil
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowIpanema, Rio de Janeiro, Brazil

Common Values

ValueCountFrequency (%)
Rio de Janeiro, Brazil 46
46.0%
Rio, Rio de Janeiro, Brazil 5
 
5.0%
Ipanema, Rio de Janeiro, Brazil 1
 
1.0%
Rio de Janeiro, Rj, Brazil 1
 
1.0%
Joatinga, Rio de Janeiro, Brazil 1
 
1.0%
Santa Teresa, Rio de Janeiro, Brazil 1
 
1.0%
Rio de Janeiro , Rio de Janeiro, Brazil 1
 
1.0%
Rio de janeiro , Rio de Janeiro, Brazil 1
 
1.0%
(Missing) 43
43.0%

Length

2024-04-28T15:05:52.699022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:52.818743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rio 64
26.0%
de 59
24.0%
janeiro 59
24.0%
brazil 57
23.2%
2
 
0.8%
ipanema 1
 
0.4%
rj 1
 
0.4%
joatinga 1
 
0.4%
santa 1
 
0.4%
teresa 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
189
14.0%
i 181
13.4%
o 124
9.2%
a 123
9.1%
e 121
9.0%
r 117
8.7%
, 68
 
5.0%
R 65
 
4.8%
n 62
 
4.6%
d 59
 
4.4%
Other values (13) 241
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
189
14.0%
i 181
13.4%
o 124
9.2%
a 123
9.1%
e 121
9.0%
r 117
8.7%
, 68
 
5.0%
R 65
 
4.8%
n 62
 
4.6%
d 59
 
4.4%
Other values (13) 241
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
189
14.0%
i 181
13.4%
o 124
9.2%
a 123
9.1%
e 121
9.0%
r 117
8.7%
, 68
 
5.0%
R 65
 
4.8%
n 62
 
4.6%
d 59
 
4.4%
Other values (13) 241
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
189
14.0%
i 181
13.4%
o 124
9.2%
a 123
9.1%
e 121
9.0%
r 117
8.7%
, 68
 
5.0%
R 65
 
4.8%
n 62
 
4.6%
d 59
 
4.4%
Other values (13) 241
17.9%
Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Copacabana
43 
Santa Teresa
11 
Ipanema
10 
Barra da Tijuca
Leblon
Other values (14)
24 

Length

Max length24
Median length15
Mean length9.71
Min length3

Characters and Unicode

Total characters971
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)7.0%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana

Common Values

ValueCountFrequency (%)
Copacabana 43
43.0%
Santa Teresa 11
 
11.0%
Ipanema 10
 
10.0%
Barra da Tijuca 6
 
6.0%
Leblon 6
 
6.0%
Flamengo 4
 
4.0%
Botafogo 3
 
3.0%
Centro 2
 
2.0%
Leme 2
 
2.0%
Jardim Botânico 2
 
2.0%
Other values (9) 11
 
11.0%

Length

2024-04-28T15:05:52.972443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copacabana 43
33.3%
teresa 11
 
8.5%
santa 11
 
8.5%
ipanema 10
 
7.8%
da 7
 
5.4%
tijuca 7
 
5.4%
barra 6
 
4.7%
leblon 6
 
4.7%
flamengo 4
 
3.1%
botafogo 3
 
2.3%
Other values (15) 21
16.3%

Most occurring characters

ValueCountFrequency (%)
a 280
28.8%
n 83
 
8.5%
o 72
 
7.4%
e 58
 
6.0%
p 55
 
5.7%
c 55
 
5.7%
b 49
 
5.0%
C 45
 
4.6%
r 35
 
3.6%
29
 
3.0%
Other values (25) 210
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 280
28.8%
n 83
 
8.5%
o 72
 
7.4%
e 58
 
6.0%
p 55
 
5.7%
c 55
 
5.7%
b 49
 
5.0%
C 45
 
4.6%
r 35
 
3.6%
29
 
3.0%
Other values (25) 210
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 280
28.8%
n 83
 
8.5%
o 72
 
7.4%
e 58
 
6.0%
p 55
 
5.7%
c 55
 
5.7%
b 49
 
5.0%
C 45
 
4.6%
r 35
 
3.6%
29
 
3.0%
Other values (25) 210
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 280
28.8%
n 83
 
8.5%
o 72
 
7.4%
e 58
 
6.0%
p 55
 
5.7%
c 55
 
5.7%
b 49
 
5.0%
C 45
 
4.6%
r 35
 
3.6%
29
 
3.0%
Other values (25) 210
21.6%

neighbourhood_group_cleansed
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

latitude
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.967036
Minimum-23.03154
Maximum-22.84208
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size932.0 B
2024-04-28T15:05:53.102575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-23.03154
5-th percentile-23.008247
Q1-22.982385
median-22.975741
Q3-22.96198
95-th percentile-22.917173
Maximum-22.84208
Range0.18946
Interquartile range (IQR)0.020405

Descriptive statistics

Standard deviation0.02841149
Coefficient of variation (CV)-0.0012370552
Kurtosis2.9387793
Mean-22.967036
Median Absolute Deviation (MAD)0.00964
Skewness1.1718422
Sum-2296.7036
Variance0.00080721275
MonotonicityNot monotonic
2024-04-28T15:05:53.243052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.98127 2
 
2.0%
-22.96599 1
 
1.0%
-22.97369 1
 
1.0%
-22.95508 1
 
1.0%
-23.01469 1
 
1.0%
-22.9754828 1
 
1.0%
-23.01524 1
 
1.0%
-22.96689 1
 
1.0%
-22.96102 1
 
1.0%
-22.91742 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
-23.03154 1
1.0%
-23.01524 1
1.0%
-23.01469 1
1.0%
-23.01147 1
1.0%
-23.01124 1
1.0%
-23.00809 1
1.0%
-23.00451 1
1.0%
-23.00291 1
1.0%
-22.99508 1
1.0%
-22.98812 1
1.0%
ValueCountFrequency (%)
-22.84208 1
1.0%
-22.9114 1
1.0%
-22.91258 1
1.0%
-22.91491 1
1.0%
-22.91666 1
1.0%
-22.9172 1
1.0%
-22.91742 1
1.0%
-22.91825 1
1.0%
-22.92099 1
1.0%
-22.92129 1
1.0%

longitude
Real number (ℝ)

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.210403
Minimum-43.47437
Maximum-43.16858
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size932.0 B
2024-04-28T15:05:53.373703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-43.47437
5-th percentile-43.364283
Q1-43.203433
median-43.19081
Q3-43.182848
95-th percentile-43.174956
Maximum-43.16858
Range0.30579
Interquartile range (IQR)0.020585

Descriptive statistics

Standard deviation0.055712981
Coefficient of variation (CV)-0.0012893419
Kurtosis7.4560728
Mean-43.210403
Median Absolute Deviation (MAD)0.0086505
Skewness-2.7289612
Sum-4321.0403
Variance0.0031039363
MonotonicityNot monotonic
2024-04-28T15:05:53.507480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.19215 2
 
2.0%
-43.18902 2
 
2.0%
-43.18969 2
 
2.0%
-43.1794 1
 
1.0%
-43.1901 1
 
1.0%
-43.19214 1
 
1.0%
-43.18251 1
 
1.0%
-43.3008 1
 
1.0%
-43.188548 1
 
1.0%
-43.30342 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
-43.47437 1
1.0%
-43.39086 1
1.0%
-43.388355 1
1.0%
-43.38321 1
1.0%
-43.37081 1
1.0%
-43.36394 1
1.0%
-43.31572 1
1.0%
-43.30873 1
1.0%
-43.30342 1
1.0%
-43.3008 1
1.0%
ValueCountFrequency (%)
-43.16858 1
1.0%
-43.17263 1
1.0%
-43.17389 1
1.0%
-43.17478 1
1.0%
-43.17488 1
1.0%
-43.17496 1
1.0%
-43.17514 1
1.0%
-43.17526 1
1.0%
-43.17563 1
1.0%
-43.17583 1
1.0%

property_type
Categorical

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Entire rental unit
56 
Private room in rental unit
16 
Entire condo
10 
Private room in home
Entire loft
 
4
Other values (6)

Length

Max length33
Median length18
Mean length18.95
Min length11

Characters and Unicode

Total characters1895
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st rowEntire condo
2nd rowEntire rental unit
3rd rowEntire loft
4th rowEntire rental unit
5th rowEntire condo

Common Values

ValueCountFrequency (%)
Entire rental unit 56
56.0%
Private room in rental unit 16
 
16.0%
Entire condo 10
 
10.0%
Private room in home 7
 
7.0%
Entire loft 4
 
4.0%
Entire home 2
 
2.0%
Private room in townhouse 1
 
1.0%
Private room in bed and breakfast 1
 
1.0%
Entire guest suite 1
 
1.0%
Private room in guesthouse 1
 
1.0%

Length

2024-04-28T15:05:53.635252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
entire 73
22.1%
rental 72
21.8%
unit 72
21.8%
private 27
 
8.2%
room 27
 
8.2%
in 27
 
8.2%
condo 10
 
3.0%
home 9
 
2.7%
loft 4
 
1.2%
guest 2
 
0.6%
Other values (6) 7
 
2.1%

Most occurring characters

ValueCountFrequency (%)
n 256
13.5%
t 255
13.5%
230
12.1%
i 201
10.6%
r 200
10.6%
e 190
10.0%
a 102
 
5.4%
o 90
 
4.7%
u 79
 
4.2%
l 76
 
4.0%
Other values (13) 216
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 256
13.5%
t 255
13.5%
230
12.1%
i 201
10.6%
r 200
10.6%
e 190
10.0%
a 102
 
5.4%
o 90
 
4.7%
u 79
 
4.2%
l 76
 
4.0%
Other values (13) 216
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 256
13.5%
t 255
13.5%
230
12.1%
i 201
10.6%
r 200
10.6%
e 190
10.0%
a 102
 
5.4%
o 90
 
4.7%
u 79
 
4.2%
l 76
 
4.0%
Other values (13) 216
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 256
13.5%
t 255
13.5%
230
12.1%
i 201
10.6%
r 200
10.6%
e 190
10.0%
a 102
 
5.4%
o 90
 
4.7%
u 79
 
4.2%
l 76
 
4.0%
Other values (13) 216
11.4%

room_type
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Entire home/apt
73 
Private room
27 

Length

Max length15
Median length15
Mean length14.19
Min length12

Characters and Unicode

Total characters1419
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 73
73.0%
Private room 27
 
27.0%

Length

2024-04-28T15:05:53.743747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:53.836402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
entire 73
36.5%
home/apt 73
36.5%
private 27
 
13.5%
room 27
 
13.5%

Most occurring characters

ValueCountFrequency (%)
t 173
12.2%
e 173
12.2%
r 127
8.9%
o 127
8.9%
i 100
 
7.0%
100
 
7.0%
m 100
 
7.0%
a 100
 
7.0%
E 73
 
5.1%
n 73
 
5.1%
Other values (5) 273
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 173
12.2%
e 173
12.2%
r 127
8.9%
o 127
8.9%
i 100
 
7.0%
100
 
7.0%
m 100
 
7.0%
a 100
 
7.0%
E 73
 
5.1%
n 73
 
5.1%
Other values (5) 273
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 173
12.2%
e 173
12.2%
r 127
8.9%
o 127
8.9%
i 100
 
7.0%
100
 
7.0%
m 100
 
7.0%
a 100
 
7.0%
E 73
 
5.1%
n 73
 
5.1%
Other values (5) 273
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 173
12.2%
e 173
12.2%
r 127
8.9%
o 127
8.9%
i 100
 
7.0%
100
 
7.0%
m 100
 
7.0%
a 100
 
7.0%
E 73
 
5.1%
n 73
 
5.1%
Other values (5) 273
19.2%

accommodates
Real number (ℝ)

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.92
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:53.919538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q12
median3
Q34.25
95-th percentile9.05
Maximum16
Range15
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.6767989
Coefficient of variation (CV)0.68285687
Kurtosis6.572748
Mean3.92
Median Absolute Deviation (MAD)1
Skewness2.3055731
Sum392
Variance7.1652525
MonotonicityNot monotonic
2024-04-28T15:05:54.062149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 31
31.0%
4 24
24.0%
3 15
15.0%
6 9
 
9.0%
5 8
 
8.0%
1 5
 
5.0%
8 2
 
2.0%
13 1
 
1.0%
9 1
 
1.0%
16 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
1 5
 
5.0%
2 31
31.0%
3 15
15.0%
4 24
24.0%
5 8
 
8.0%
6 9
 
9.0%
8 2
 
2.0%
9 1
 
1.0%
10 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
16 1
 
1.0%
14 1
 
1.0%
13 1
 
1.0%
12 1
 
1.0%
10 1
 
1.0%
9 1
 
1.0%
8 2
 
2.0%
6 9
 
9.0%
5 8
 
8.0%
4 24
24.0%

bathrooms
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

bathrooms_text
Categorical

Distinct13
Distinct (%)13.1%
Missing1
Missing (%)1.0%
Memory size932.0 B
1 bath
50 
2 baths
1.5 baths
1 shared bath
2.5 baths
Other values (8)
20 

Length

Max length16
Median length6
Mean length7.6767677
Min length6

Characters and Unicode

Total characters760
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row1 bath
2nd row1 bath
3rd row1.5 baths
4th row1 bath
5th row1 bath

Common Values

ValueCountFrequency (%)
1 bath 50
50.0%
2 baths 9
 
9.0%
1.5 baths 8
 
8.0%
1 shared bath 6
 
6.0%
2.5 baths 6
 
6.0%
1 private bath 5
 
5.0%
3 baths 4
 
4.0%
7 baths 3
 
3.0%
5 baths 3
 
3.0%
4 baths 2
 
2.0%
Other values (3) 3
 
3.0%

Length

2024-04-28T15:05:54.204985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 61
28.9%
bath 61
28.9%
baths 38
18.0%
2 9
 
4.3%
1.5 8
 
3.8%
shared 8
 
3.8%
2.5 7
 
3.3%
private 5
 
2.4%
3 4
 
1.9%
5 4
 
1.9%
Other values (3) 6
 
2.8%

Most occurring characters

ValueCountFrequency (%)
a 112
14.7%
112
14.7%
h 107
14.1%
t 104
13.7%
b 99
13.0%
1 69
9.1%
s 46
6.1%
5 20
 
2.6%
. 16
 
2.1%
2 16
 
2.1%
Other values (9) 59
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 112
14.7%
112
14.7%
h 107
14.1%
t 104
13.7%
b 99
13.0%
1 69
9.1%
s 46
6.1%
5 20
 
2.6%
. 16
 
2.1%
2 16
 
2.1%
Other values (9) 59
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 112
14.7%
112
14.7%
h 107
14.1%
t 104
13.7%
b 99
13.0%
1 69
9.1%
s 46
6.1%
5 20
 
2.6%
. 16
 
2.1%
2 16
 
2.1%
Other values (9) 59
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 112
14.7%
112
14.7%
h 107
14.1%
t 104
13.7%
b 99
13.0%
1 69
9.1%
s 46
6.1%
5 20
 
2.6%
. 16
 
2.1%
2 16
 
2.1%
Other values (9) 59
7.8%

bedrooms
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

beds
Real number (ℝ)

Distinct10
Distinct (%)10.1%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.6464646
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:54.306833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4172557
Coefficient of variation (CV)0.91339051
Kurtosis9.871837
Mean2.6464646
Median Absolute Deviation (MAD)1
Skewness2.6947717
Sum262
Variance5.8431251
MonotonicityNot monotonic
2024-04-28T15:05:54.419443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 40
40.0%
2 24
24.0%
3 14
 
14.0%
4 8
 
8.0%
7 5
 
5.0%
5 3
 
3.0%
10 2
 
2.0%
16 1
 
1.0%
6 1
 
1.0%
8 1
 
1.0%
(Missing) 1
 
1.0%
ValueCountFrequency (%)
1 40
40.0%
2 24
24.0%
3 14
 
14.0%
4 8
 
8.0%
5 3
 
3.0%
6 1
 
1.0%
7 5
 
5.0%
8 1
 
1.0%
10 2
 
2.0%
16 1
 
1.0%
ValueCountFrequency (%)
16 1
 
1.0%
10 2
 
2.0%
8 1
 
1.0%
7 5
 
5.0%
6 1
 
1.0%
5 3
 
3.0%
4 8
 
8.0%
3 14
 
14.0%
2 24
24.0%
1 40
40.0%

amenities
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
[]
100 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 100
100.0%

Length

2024-04-28T15:05:54.533910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:54.623363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

Most occurring characters

ValueCountFrequency (%)
[ 100
50.0%
] 100
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 100
50.0%
] 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 100
50.0%
] 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 100
50.0%
] 100
50.0%

price
Text

MISSING 

Distinct89
Distinct (%)90.8%
Missing2
Missing (%)2.0%
Memory size932.0 B
2024-04-28T15:05:54.876822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.5
Min length6

Characters and Unicode

Total characters735
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)84.7%

Sample

1st row$1,357.00
2nd row$865.00
3rd row$373.00
4th row$1,701.00
5th row$366.00
ValueCountFrequency (%)
500.00 5
 
5.1%
250.00 2
 
2.0%
734.00 2
 
2.0%
300.00 2
 
2.0%
437.00 2
 
2.0%
169.00 2
 
2.0%
409.00 1
 
1.0%
1,701.00 1
 
1.0%
366.00 1
 
1.0%
368.00 1
 
1.0%
Other values (79) 79
80.6%
2024-04-28T15:05:55.458736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 249
33.9%
$ 98
 
13.3%
. 98
 
13.3%
1 38
 
5.2%
3 37
 
5.0%
6 35
 
4.8%
4 30
 
4.1%
5 29
 
3.9%
7 29
 
3.9%
9 25
 
3.4%
Other values (3) 67
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 249
33.9%
$ 98
 
13.3%
. 98
 
13.3%
1 38
 
5.2%
3 37
 
5.0%
6 35
 
4.8%
4 30
 
4.1%
5 29
 
3.9%
7 29
 
3.9%
9 25
 
3.4%
Other values (3) 67
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 249
33.9%
$ 98
 
13.3%
. 98
 
13.3%
1 38
 
5.2%
3 37
 
5.0%
6 35
 
4.8%
4 30
 
4.1%
5 29
 
3.9%
7 29
 
3.9%
9 25
 
3.4%
Other values (3) 67
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 249
33.9%
$ 98
 
13.3%
. 98
 
13.3%
1 38
 
5.2%
3 37
 
5.0%
6 35
 
4.8%
4 30
 
4.1%
5 29
 
3.9%
7 29
 
3.9%
9 25
 
3.4%
Other values (3) 67
 
9.1%

minimum_nights
Real number (ℝ)

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:55.600606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile6.7
Maximum78
Range77
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.8454748
Coefficient of variation (CV)2.1060654
Kurtosis50.753959
Mean4.2
Median Absolute Deviation (MAD)1
Skewness6.6249504
Sum420
Variance78.242424
MonotonicityNot monotonic
2024-04-28T15:05:55.715887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 33
33.0%
2 25
25.0%
1 22
22.0%
5 7
 
7.0%
4 7
 
7.0%
30 2
 
2.0%
20 1
 
1.0%
6 1
 
1.0%
22 1
 
1.0%
78 1
 
1.0%
ValueCountFrequency (%)
1 22
22.0%
2 25
25.0%
3 33
33.0%
4 7
 
7.0%
5 7
 
7.0%
6 1
 
1.0%
20 1
 
1.0%
22 1
 
1.0%
30 2
 
2.0%
78 1
 
1.0%
ValueCountFrequency (%)
78 1
 
1.0%
30 2
 
2.0%
22 1
 
1.0%
20 1
 
1.0%
6 1
 
1.0%
5 7
 
7.0%
4 7
 
7.0%
3 33
33.0%
2 25
25.0%
1 22
22.0%

maximum_nights
Real number (ℝ)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295.59
Minimum7
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:55.843406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13.8
Q130
median90
Q3365
95-th percentile1125
Maximum1125
Range1118
Interquartile range (IQR)335

Descriptive statistics

Standard deviation395.61667
Coefficient of variation (CV)1.3383967
Kurtosis0.22806384
Mean295.59
Median Absolute Deviation (MAD)60
Skewness1.3622367
Sum29559
Variance156512.55
MonotonicityNot monotonic
2024-04-28T15:05:55.974808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
90 21
21.0%
30 17
17.0%
1125 15
15.0%
89 8
 
8.0%
365 6
 
6.0%
180 4
 
4.0%
28 3
 
3.0%
60 3
 
3.0%
730 3
 
3.0%
7 2
 
2.0%
Other values (15) 18
18.0%
ValueCountFrequency (%)
7 2
 
2.0%
9 1
 
1.0%
10 2
 
2.0%
14 2
 
2.0%
15 1
 
1.0%
20 1
 
1.0%
21 1
 
1.0%
28 3
 
3.0%
29 1
 
1.0%
30 17
17.0%
ValueCountFrequency (%)
1125 15
15.0%
760 2
 
2.0%
750 1
 
1.0%
730 3
 
3.0%
520 1
 
1.0%
500 1
 
1.0%
420 1
 
1.0%
365 6
 
6.0%
180 4
 
4.0%
150 1
 
1.0%

minimum_minimum_nights
Real number (ℝ)

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.98
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:56.103349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6.7
Maximum78
Range77
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.896691
Coefficient of variation (CV)2.2353495
Kurtosis50.200504
Mean3.98
Median Absolute Deviation (MAD)1
Skewness6.5821906
Sum398
Variance79.151111
MonotonicityNot monotonic
2024-04-28T15:05:56.246964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 32
32.0%
2 26
26.0%
3 24
24.0%
5 8
 
8.0%
4 4
 
4.0%
30 2
 
2.0%
20 1
 
1.0%
6 1
 
1.0%
22 1
 
1.0%
78 1
 
1.0%
ValueCountFrequency (%)
1 32
32.0%
2 26
26.0%
3 24
24.0%
4 4
 
4.0%
5 8
 
8.0%
6 1
 
1.0%
20 1
 
1.0%
22 1
 
1.0%
30 2
 
2.0%
78 1
 
1.0%
ValueCountFrequency (%)
78 1
 
1.0%
30 2
 
2.0%
22 1
 
1.0%
20 1
 
1.0%
6 1
 
1.0%
5 8
 
8.0%
4 4
 
4.0%
3 24
24.0%
2 26
26.0%
1 32
32.0%

maximum_minimum_nights
Real number (ℝ)

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.14
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:56.377821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10.5
Maximum78
Range77
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.8077331
Coefficient of variation (CV)1.7135667
Kurtosis48.770802
Mean5.14
Median Absolute Deviation (MAD)2
Skewness6.3948795
Sum514
Variance77.576162
MonotonicityNot monotonic
2024-04-28T15:05:56.495535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 24
24.0%
5 21
21.0%
1 18
18.0%
2 13
13.0%
4 8
 
8.0%
7 5
 
5.0%
6 3
 
3.0%
9 2
 
2.0%
30 2
 
2.0%
20 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
1 18
18.0%
2 13
13.0%
3 24
24.0%
4 8
 
8.0%
5 21
21.0%
6 3
 
3.0%
7 5
 
5.0%
9 2
 
2.0%
10 1
 
1.0%
20 1
 
1.0%
ValueCountFrequency (%)
78 1
 
1.0%
30 2
 
2.0%
22 1
 
1.0%
20 1
 
1.0%
10 1
 
1.0%
9 2
 
2.0%
7 5
 
5.0%
6 3
 
3.0%
5 21
21.0%
4 8
 
8.0%

minimum_maximum_nights
Real number (ℝ)

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474.58
Minimum1
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:56.619228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.95
Q152.5
median121
Q31125
95-th percentile1125
Maximum1125
Range1124
Interquartile range (IQR)1072.5

Descriptive statistics

Standard deviation488.96006
Coefficient of variation (CV)1.0303006
Kurtosis-1.667577
Mean474.58
Median Absolute Deviation (MAD)113
Skewness0.46483146
Sum47458
Variance239081.94
MonotonicityNot monotonic
2024-04-28T15:05:56.736068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1125 33
33.0%
90 16
16.0%
30 13
 
13.0%
365 6
 
6.0%
89 5
 
5.0%
180 3
 
3.0%
60 3
 
3.0%
28 2
 
2.0%
730 2
 
2.0%
1 2
 
2.0%
Other values (12) 15
15.0%
ValueCountFrequency (%)
1 2
 
2.0%
7 2
 
2.0%
9 1
 
1.0%
10 2
 
2.0%
14 1
 
1.0%
20 1
 
1.0%
21 1
 
1.0%
28 2
 
2.0%
30 13
13.0%
60 3
 
3.0%
ValueCountFrequency (%)
1125 33
33.0%
760 2
 
2.0%
750 1
 
1.0%
730 2
 
2.0%
520 1
 
1.0%
500 1
 
1.0%
365 6
 
6.0%
180 3
 
3.0%
150 1
 
1.0%
92 1
 
1.0%

maximum_maximum_nights
Real number (ℝ)

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.09
Minimum7
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:56.847769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.95
Q181.75
median180
Q31125
95-th percentile1125
Maximum1125
Range1118
Interquartile range (IQR)1043.25

Descriptive statistics

Standard deviation494.13054
Coefficient of variation (CV)0.97252562
Kurtosis-1.7804919
Mean508.09
Median Absolute Deviation (MAD)172
Skewness0.33908625
Sum50809
Variance244164.99
MonotonicityNot monotonic
2024-04-28T15:05:56.960289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1125 36
36.0%
90 16
16.0%
30 12
 
12.0%
365 6
 
6.0%
89 5
 
5.0%
180 3
 
3.0%
60 3
 
3.0%
28 2
 
2.0%
730 2
 
2.0%
10 2
 
2.0%
Other values (12) 13
 
13.0%
ValueCountFrequency (%)
7 1
 
1.0%
9 1
 
1.0%
10 2
 
2.0%
14 1
 
1.0%
15 1
 
1.0%
20 1
 
1.0%
21 1
 
1.0%
28 2
 
2.0%
30 12
12.0%
60 3
 
3.0%
ValueCountFrequency (%)
1125 36
36.0%
760 2
 
2.0%
750 1
 
1.0%
730 2
 
2.0%
520 1
 
1.0%
500 1
 
1.0%
365 6
 
6.0%
180 3
 
3.0%
150 1
 
1.0%
92 1
 
1.0%

minimum_nights_avg_ntm
Real number (ℝ)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.357
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:57.074815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33.225
95-th percentile7.65
Maximum78
Range77
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation8.8295676
Coefficient of variation (CV)2.0265246
Kurtosis50.651176
Mean4.357
Median Absolute Deviation (MAD)1
Skewness6.6083156
Sum435.7
Variance77.961264
MonotonicityNot monotonic
2024-04-28T15:05:57.191329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 22
22.0%
1 19
19.0%
2 13
13.0%
5 7
 
7.0%
4 7
 
7.0%
2.1 6
 
6.0%
3.1 3
 
3.0%
2.3 3
 
3.0%
2.7 2
 
2.0%
30 2
 
2.0%
Other values (15) 16
16.0%
ValueCountFrequency (%)
1 19
19.0%
1.3 1
 
1.0%
1.9 1
 
1.0%
2 13
13.0%
2.1 6
 
6.0%
2.2 1
 
1.0%
2.3 3
 
3.0%
2.7 2
 
2.0%
2.8 1
 
1.0%
2.9 1
 
1.0%
ValueCountFrequency (%)
78 1
 
1.0%
30 2
 
2.0%
22 1
 
1.0%
20 1
 
1.0%
7 1
 
1.0%
6 1
 
1.0%
5.2 1
 
1.0%
5 7
7.0%
4.9 1
 
1.0%
4 7
7.0%

maximum_nights_avg_ntm
Real number (ℝ)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494.424
Minimum7
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:57.318862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.665
Q181.75
median180
Q31125
95-th percentile1125
Maximum1125
Range1118
Interquartile range (IQR)1043.25

Descriptive statistics

Standard deviation488.38064
Coefficient of variation (CV)0.98777696
Kurtosis-1.7276972
Mean494.424
Median Absolute Deviation (MAD)170.5
Skewness0.39060018
Sum49442.4
Variance238515.65
MonotonicityNot monotonic
2024-04-28T15:05:57.450430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1125 33
33.0%
90 16
16.0%
30 12
 
12.0%
365 6
 
6.0%
89 5
 
5.0%
180 3
 
3.0%
60 3
 
3.0%
28 2
 
2.0%
730 2
 
2.0%
10 2
 
2.0%
Other values (15) 16
16.0%
ValueCountFrequency (%)
7 1
 
1.0%
9 1
 
1.0%
10 2
 
2.0%
14 1
 
1.0%
14.7 1
 
1.0%
20 1
 
1.0%
21 1
 
1.0%
28 2
 
2.0%
30 12
12.0%
60 3
 
3.0%
ValueCountFrequency (%)
1125 33
33.0%
1114.7 1
 
1.0%
782.7 1
 
1.0%
760 2
 
2.0%
750 1
 
1.0%
730 2
 
2.0%
520 1
 
1.0%
500 1
 
1.0%
365 6
 
6.0%
180 3
 
3.0%

calendar_updated
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

has_availability
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.0%
Missing2
Missing (%)2.0%
Memory size332.0 B
True
98 
(Missing)
 
2
ValueCountFrequency (%)
True 98
98.0%
(Missing) 2
 
2.0%
2024-04-28T15:05:57.555957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

availability_30
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.8
Minimum0
Maximum30
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:57.677481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median5.5
Q319.25
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation10.982999
Coefficient of variation (CV)1.0169443
Kurtosis-1.2039074
Mean10.8
Median Absolute Deviation (MAD)5.5
Skewness0.5882097
Sum1080
Variance120.62626
MonotonicityNot monotonic
2024-04-28T15:05:57.859170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 25
25.0%
30 10
 
10.0%
3 7
 
7.0%
5 5
 
5.0%
2 5
 
5.0%
1 5
 
5.0%
19 4
 
4.0%
24 3
 
3.0%
18 3
 
3.0%
6 3
 
3.0%
Other values (19) 30
30.0%
ValueCountFrequency (%)
0 25
25.0%
1 5
 
5.0%
2 5
 
5.0%
3 7
 
7.0%
4 3
 
3.0%
5 5
 
5.0%
6 3
 
3.0%
7 1
 
1.0%
8 2
 
2.0%
9 1
 
1.0%
ValueCountFrequency (%)
30 10
10.0%
29 2
 
2.0%
28 2
 
2.0%
27 1
 
1.0%
26 1
 
1.0%
25 1
 
1.0%
24 3
 
3.0%
23 1
 
1.0%
22 1
 
1.0%
21 2
 
2.0%

availability_60
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.58
Minimum0
Maximum60
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:58.004208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median19.5
Q339.25
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation21.143655
Coefficient of variation (CV)0.89667748
Kurtosis-1.1841256
Mean23.58
Median Absolute Deviation (MAD)18.5
Skewness0.47326156
Sum2358
Variance447.05414
MonotonicityNot monotonic
2024-04-28T15:05:58.138352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 20
20.0%
60 8
 
8.0%
9 6
 
6.0%
38 4
 
4.0%
51 4
 
4.0%
7 3
 
3.0%
20 3
 
3.0%
19 3
 
3.0%
5 2
 
2.0%
2 2
 
2.0%
Other values (33) 45
45.0%
ValueCountFrequency (%)
0 20
20.0%
1 2
 
2.0%
2 2
 
2.0%
3 1
 
1.0%
4 1
 
1.0%
5 2
 
2.0%
6 2
 
2.0%
7 3
 
3.0%
9 6
 
6.0%
10 1
 
1.0%
ValueCountFrequency (%)
60 8
8.0%
59 2
 
2.0%
58 2
 
2.0%
57 1
 
1.0%
54 2
 
2.0%
52 1
 
1.0%
51 4
4.0%
46 1
 
1.0%
45 1
 
1.0%
44 1
 
1.0%

availability_90
Real number (ℝ)

ZEROS 

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.13
Minimum0
Maximum90
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:58.273401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median40
Q369
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)59

Descriptive statistics

Standard deviation32.111125
Coefficient of variation (CV)0.78072271
Kurtosis-1.3812719
Mean41.13
Median Absolute Deviation (MAD)29.5
Skewness0.12127216
Sum4113
Variance1031.1243
MonotonicityNot monotonic
2024-04-28T15:05:58.434801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
19.0%
90 8
 
8.0%
81 4
 
4.0%
26 3
 
3.0%
88 2
 
2.0%
63 2
 
2.0%
49 2
 
2.0%
69 2
 
2.0%
34 2
 
2.0%
54 2
 
2.0%
Other values (45) 54
54.0%
ValueCountFrequency (%)
0 19
19.0%
1 1
 
1.0%
2 1
 
1.0%
3 1
 
1.0%
4 1
 
1.0%
6 1
 
1.0%
10 2
 
2.0%
11 1
 
1.0%
12 1
 
1.0%
13 2
 
2.0%
ValueCountFrequency (%)
90 8
8.0%
89 2
 
2.0%
88 2
 
2.0%
87 1
 
1.0%
84 2
 
2.0%
82 1
 
1.0%
81 4
4.0%
76 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%

availability_365
Real number (ℝ)

ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.47
Minimum0
Maximum365
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:58.591266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median230.5
Q3319
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)279

Descriptive statistics

Standard deviation137.36153
Coefficient of variation (CV)0.72497775
Kurtosis-1.5853573
Mean189.47
Median Absolute Deviation (MAD)126
Skewness-0.17502219
Sum18947
Variance18868.191
MonotonicityNot monotonic
2024-04-28T15:05:58.732266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
12.0%
365 8
 
8.0%
319 3
 
3.0%
269 2
 
2.0%
356 2
 
2.0%
31 2
 
2.0%
264 2
 
2.0%
363 2
 
2.0%
171 2
 
2.0%
44 2
 
2.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
0 12
12.0%
1 1
 
1.0%
2 1
 
1.0%
3 1
 
1.0%
10 1
 
1.0%
16 1
 
1.0%
18 1
 
1.0%
20 1
 
1.0%
26 1
 
1.0%
30 1
 
1.0%
ValueCountFrequency (%)
365 8
8.0%
364 1
 
1.0%
363 2
 
2.0%
362 1
 
1.0%
357 1
 
1.0%
356 2
 
2.0%
351 1
 
1.0%
345 1
 
1.0%
339 1
 
1.0%
338 1
 
1.0%
Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-27
40 
2023-12-26
34 
2023-12-28
17 
2023-12-30
2023-12-29
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2023-12-27
2nd row2023-12-27
3rd row2023-12-27
4th row2023-12-27
5th row2023-12-27

Common Values

ValueCountFrequency (%)
2023-12-27 40
40.0%
2023-12-26 34
34.0%
2023-12-28 17
17.0%
2023-12-30 8
 
8.0%
2023-12-29 1
 
1.0%

Length

2024-04-28T15:05:58.863318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:05:59.007875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-27 40
40.0%
2023-12-26 34
34.0%
2023-12-28 17
17.0%
2023-12-30 8
 
8.0%
2023-12-29 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 392
39.2%
- 200
20.0%
0 108
 
10.8%
3 108
 
10.8%
1 100
 
10.0%
7 40
 
4.0%
6 34
 
3.4%
8 17
 
1.7%
9 1
 
0.1%

number_of_reviews
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.38
Minimum0
Maximum555
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:59.140606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.75
median76
Q3160.25
95-th percentile353.65
Maximum555
Range555
Interquartile range (IQR)143.5

Descriptive statistics

Standard deviation121.17662
Coefficient of variation (CV)1.0782757
Kurtosis1.9164457
Mean112.38
Median Absolute Deviation (MAD)71.5
Skewness1.4457571
Sum11238
Variance14683.773
MonotonicityNot monotonic
2024-04-28T15:05:59.297425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
10.0%
24 4
 
4.0%
12 3
 
3.0%
4 3
 
3.0%
37 2
 
2.0%
29 2
 
2.0%
7 2
 
2.0%
74 2
 
2.0%
2 2
 
2.0%
17 2
 
2.0%
Other values (67) 68
68.0%
ValueCountFrequency (%)
0 10
10.0%
1 1
 
1.0%
2 2
 
2.0%
3 1
 
1.0%
4 3
 
3.0%
5 1
 
1.0%
7 2
 
2.0%
11 1
 
1.0%
12 3
 
3.0%
16 1
 
1.0%
ValueCountFrequency (%)
555 1
1.0%
463 1
1.0%
454 1
1.0%
431 1
1.0%
404 1
1.0%
351 1
1.0%
313 1
1.0%
311 1
1.0%
293 1
1.0%
281 1
1.0%

number_of_reviews_ltm
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7
Minimum0
Maximum65
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:59.449739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q325.25
95-th percentile36
Maximum65
Range65
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation15.25507
Coefficient of variation (CV)1.1135088
Kurtosis1.1552853
Mean13.7
Median Absolute Deviation (MAD)8
Skewness1.1728045
Sum1370
Variance232.71717
MonotonicityNot monotonic
2024-04-28T15:05:59.620629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 31
31.0%
3 5
 
5.0%
29 4
 
4.0%
4 4
 
4.0%
6 4
 
4.0%
22 4
 
4.0%
36 4
 
4.0%
31 4
 
4.0%
27 3
 
3.0%
13 3
 
3.0%
Other values (24) 34
34.0%
ValueCountFrequency (%)
0 31
31.0%
1 2
 
2.0%
2 1
 
1.0%
3 5
 
5.0%
4 4
 
4.0%
5 1
 
1.0%
6 4
 
4.0%
7 1
 
1.0%
8 3
 
3.0%
10 3
 
3.0%
ValueCountFrequency (%)
65 1
 
1.0%
64 1
 
1.0%
54 1
 
1.0%
51 1
 
1.0%
36 4
4.0%
34 1
 
1.0%
33 1
 
1.0%
31 4
4.0%
30 2
2.0%
29 4
4.0%

number_of_reviews_l30d
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74
Minimum0
Maximum6
Zeros64
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:05:59.741961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1859446
Coefficient of variation (CV)1.6026279
Kurtosis3.4443115
Mean0.74
Median Absolute Deviation (MAD)0
Skewness1.7819181
Sum74
Variance1.4064646
MonotonicityNot monotonic
2024-04-28T15:05:59.842207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 64
64.0%
2 15
 
15.0%
1 12
 
12.0%
3 6
 
6.0%
4 2
 
2.0%
6 1
 
1.0%
ValueCountFrequency (%)
0 64
64.0%
1 12
 
12.0%
2 15
 
15.0%
3 6
 
6.0%
4 2
 
2.0%
6 1
 
1.0%
ValueCountFrequency (%)
6 1
 
1.0%
4 2
 
2.0%
3 6
 
6.0%
2 15
 
15.0%
1 12
 
12.0%
0 64
64.0%

first_review
Date

MISSING 

Distinct81
Distinct (%)90.0%
Missing10
Missing (%)10.0%
Memory size932.0 B
Minimum2010-06-07 00:00:00
Maximum2021-08-12 00:00:00
2024-04-28T15:05:59.961915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:06:00.107598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

last_review
Date

MISSING 

Distinct61
Distinct (%)67.8%
Missing10
Missing (%)10.0%
Memory size932.0 B
Minimum2012-02-21 00:00:00
Maximum2023-12-25 00:00:00
2024-04-28T15:06:00.251908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:06:00.396152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

review_scores_rating
Real number (ℝ)

MISSING 

Distinct46
Distinct (%)51.1%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.7532222
Minimum4
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:00.537444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.268
Q14.685
median4.8
Q34.88
95-th percentile5
Maximum5
Range1
Interquartile range (IQR)0.195

Descriptive statistics

Standard deviation0.20825561
Coefficient of variation (CV)0.043813565
Kurtosis2.4589278
Mean4.7532222
Median Absolute Deviation (MAD)0.095
Skewness-1.5093246
Sum427.79
Variance0.0433704
MonotonicityNot monotonic
2024-04-28T15:06:00.670504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
5 9
 
9.0%
4.78 5
 
5.0%
4.76 5
 
5.0%
4.8 4
 
4.0%
4.86 4
 
4.0%
4.92 4
 
4.0%
4.81 4
 
4.0%
4.82 3
 
3.0%
4.79 3
 
3.0%
4.87 3
 
3.0%
Other values (36) 46
46.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
4 1
1.0%
4.14 1
1.0%
4.19 1
1.0%
4.21 1
1.0%
4.25 1
1.0%
4.29 1
1.0%
4.34 1
1.0%
4.4 1
1.0%
4.5 1
1.0%
4.51 1
1.0%
ValueCountFrequency (%)
5 9
9.0%
4.98 2
 
2.0%
4.96 1
 
1.0%
4.95 1
 
1.0%
4.92 4
4.0%
4.91 1
 
1.0%
4.9 2
 
2.0%
4.89 2
 
2.0%
4.88 2
 
2.0%
4.87 3
 
3.0%

review_scores_accuracy
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)44.4%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.7697778
Minimum3.88
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:00.816966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.88
5-th percentile4.25
Q14.7225
median4.83
Q34.9075
95-th percentile5
Maximum5
Range1.12
Interquartile range (IQR)0.185

Descriptive statistics

Standard deviation0.22836487
Coefficient of variation (CV)0.047877464
Kurtosis5.0206713
Mean4.7697778
Median Absolute Deviation (MAD)0.095
Skewness-2.13647
Sum429.28
Variance0.052150512
MonotonicityNot monotonic
2024-04-28T15:06:00.942941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 9
 
9.0%
4.83 7
 
7.0%
4.87 6
 
6.0%
4.86 5
 
5.0%
4.75 4
 
4.0%
4.93 4
 
4.0%
4.7 4
 
4.0%
4.73 4
 
4.0%
4.92 3
 
3.0%
4.72 3
 
3.0%
Other values (30) 41
41.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
3.88 1
1.0%
3.92 1
1.0%
4 1
1.0%
4.24 1
1.0%
4.25 2
2.0%
4.33 1
1.0%
4.39 1
1.0%
4.43 1
1.0%
4.57 1
1.0%
4.58 1
1.0%
ValueCountFrequency (%)
5 9
9.0%
4.98 1
 
1.0%
4.95 3
 
3.0%
4.94 1
 
1.0%
4.93 4
4.0%
4.92 3
 
3.0%
4.91 2
 
2.0%
4.9 2
 
2.0%
4.89 1
 
1.0%
4.88 1
 
1.0%

review_scores_cleanliness
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)55.6%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.7
Minimum3.67
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:01.075126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.67
5-th percentile4.206
Q14.595
median4.765
Q34.8875
95-th percentile5
Maximum5
Range1.33
Interquartile range (IQR)0.2925

Descriptive statistics

Standard deviation0.25547048
Coefficient of variation (CV)0.054355422
Kurtosis2.3296178
Mean4.7
Median Absolute Deviation (MAD)0.135
Skewness-1.383327
Sum423
Variance0.065265169
MonotonicityNot monotonic
2024-04-28T15:06:01.216362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6
 
6.0%
4.93 5
 
5.0%
4.79 5
 
5.0%
4.74 4
 
4.0%
4.9 3
 
3.0%
4.67 3
 
3.0%
4.96 3
 
3.0%
4.71 3
 
3.0%
4.86 3
 
3.0%
4.88 3
 
3.0%
Other values (40) 52
52.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
3.67 1
1.0%
4.01 1
1.0%
4.13 1
1.0%
4.15 1
1.0%
4.17 1
1.0%
4.25 2
2.0%
4.31 1
1.0%
4.33 2
2.0%
4.36 1
1.0%
4.4 2
2.0%
ValueCountFrequency (%)
5 6
6.0%
4.99 1
 
1.0%
4.96 3
3.0%
4.95 1
 
1.0%
4.94 1
 
1.0%
4.93 5
5.0%
4.92 1
 
1.0%
4.9 3
3.0%
4.89 2
 
2.0%
4.88 3
3.0%

review_scores_checkin
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)32.2%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.8835556
Minimum4.25
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:01.335297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.25
5-th percentile4.7145
Q14.84
median4.91
Q34.96
95-th percentile5
Maximum5
Range0.75
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.12123254
Coefficient of variation (CV)0.024824646
Kurtosis11.654055
Mean4.8835556
Median Absolute Deviation (MAD)0.06
Skewness-2.8143737
Sum439.52
Variance0.014697328
MonotonicityNot monotonic
2024-04-28T15:06:01.463338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 12
 
12.0%
4.97 8
 
8.0%
4.91 7
 
7.0%
4.96 7
 
7.0%
4.9 6
 
6.0%
4.83 5
 
5.0%
4.88 4
 
4.0%
4.89 4
 
4.0%
4.86 3
 
3.0%
4.8 3
 
3.0%
Other values (19) 31
31.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
4.25 1
1.0%
4.33 1
1.0%
4.67 1
1.0%
4.69 1
1.0%
4.71 1
1.0%
4.72 1
1.0%
4.73 2
2.0%
4.75 1
1.0%
4.78 2
2.0%
4.79 1
1.0%
ValueCountFrequency (%)
5 12
12.0%
4.99 1
 
1.0%
4.97 8
8.0%
4.96 7
7.0%
4.95 3
 
3.0%
4.94 2
 
2.0%
4.93 3
 
3.0%
4.92 3
 
3.0%
4.91 7
7.0%
4.9 6
6.0%

review_scores_communication
Real number (ℝ)

MISSING 

Distinct32
Distinct (%)35.6%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.883
Minimum4.52
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:01.591926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.52
5-th percentile4.648
Q14.83
median4.91
Q34.96
95-th percentile5
Maximum5
Range0.48
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.11125888
Coefficient of variation (CV)0.022784945
Kurtosis1.5949836
Mean4.883
Median Absolute Deviation (MAD)0.06
Skewness-1.3504718
Sum439.47
Variance0.012378539
MonotonicityNot monotonic
2024-04-28T15:06:01.714674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5 12
 
12.0%
4.96 7
 
7.0%
4.88 6
 
6.0%
4.93 5
 
5.0%
4.95 5
 
5.0%
4.97 5
 
5.0%
4.89 4
 
4.0%
4.83 4
 
4.0%
4.98 4
 
4.0%
4.87 4
 
4.0%
Other values (22) 34
34.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
4.52 1
1.0%
4.56 1
1.0%
4.57 1
1.0%
4.58 1
1.0%
4.63 1
1.0%
4.67 1
1.0%
4.7 1
1.0%
4.73 1
1.0%
4.74 2
2.0%
4.75 2
2.0%
ValueCountFrequency (%)
5 12
12.0%
4.99 1
 
1.0%
4.98 4
 
4.0%
4.97 5
5.0%
4.96 7
7.0%
4.95 5
5.0%
4.94 2
 
2.0%
4.93 5
5.0%
4.92 3
 
3.0%
4.91 3
 
3.0%

review_scores_location
Real number (ℝ)

MISSING 

Distinct38
Distinct (%)42.2%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.8276667
Minimum4.25
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:01.836550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.25
5-th percentile4.4345
Q14.75
median4.89
Q34.94
95-th percentile5
Maximum5
Range0.75
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.17017539
Coefficient of variation (CV)0.035250029
Kurtosis2.2552069
Mean4.8276667
Median Absolute Deviation (MAD)0.07
Skewness-1.5884492
Sum434.49
Variance0.028959663
MonotonicityNot monotonic
2024-04-28T15:06:01.961646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5 7
 
7.0%
4.93 7
 
7.0%
4.96 5
 
5.0%
4.91 5
 
5.0%
4.89 5
 
5.0%
4.75 5
 
5.0%
4.92 4
 
4.0%
4.95 4
 
4.0%
4.76 3
 
3.0%
4.94 3
 
3.0%
Other values (28) 42
42.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
4.25 1
1.0%
4.31 1
1.0%
4.33 1
1.0%
4.37 1
1.0%
4.43 1
1.0%
4.44 1
1.0%
4.53 1
1.0%
4.57 1
1.0%
4.58 2
2.0%
4.62 1
1.0%
ValueCountFrequency (%)
5 7
7.0%
4.99 1
 
1.0%
4.98 2
 
2.0%
4.97 3
3.0%
4.96 5
5.0%
4.95 4
4.0%
4.94 3
3.0%
4.93 7
7.0%
4.92 4
4.0%
4.91 5
5.0%

review_scores_value
Real number (ℝ)

MISSING 

Distinct47
Distinct (%)52.2%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.6606667
Minimum4
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:02.103947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.23
Q14.5625
median4.705
Q34.78
95-th percentile4.923
Maximum5
Range1
Interquartile range (IQR)0.2175

Descriptive statistics

Standard deviation0.19892859
Coefficient of variation (CV)0.042682433
Kurtosis1.4396919
Mean4.6606667
Median Absolute Deviation (MAD)0.085
Skewness-1.0786436
Sum419.46
Variance0.039572584
MonotonicityNot monotonic
2024-04-28T15:06:02.236914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4.67 6
 
6.0%
4.78 6
 
6.0%
4.75 6
 
6.0%
4.77 4
 
4.0%
4.79 4
 
4.0%
4.63 3
 
3.0%
4.73 3
 
3.0%
4.5 3
 
3.0%
5 3
 
3.0%
4.56 2
 
2.0%
Other values (37) 50
50.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
4 1
1.0%
4.08 1
1.0%
4.19 1
1.0%
4.2 1
1.0%
4.23 2
2.0%
4.29 1
1.0%
4.33 1
1.0%
4.38 1
1.0%
4.43 1
1.0%
4.44 1
1.0%
ValueCountFrequency (%)
5 3
3.0%
4.97 1
 
1.0%
4.95 1
 
1.0%
4.89 2
2.0%
4.88 1
 
1.0%
4.86 2
2.0%
4.85 1
 
1.0%
4.84 1
 
1.0%
4.83 2
2.0%
4.82 1
 
1.0%

license
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B
Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
80 
True
20 
ValueCountFrequency (%)
False 80
80.0%
True 20
 
20.0%
2024-04-28T15:06:02.340350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.38
Minimum1
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:02.417037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8.1
Maximum142
Range141
Interquartile range (IQR)3

Descriptive statistics

Standard deviation14.151975
Coefficient of variation (CV)3.2310444
Kurtosis92.890901
Mean4.38
Median Absolute Deviation (MAD)1
Skewness9.4835238
Sum438
Variance200.27838
MonotonicityNot monotonic
2024-04-28T15:06:02.514606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 38
38.0%
2 18
18.0%
3 13
 
13.0%
5 9
 
9.0%
4 8
 
8.0%
6 6
 
6.0%
10 3
 
3.0%
7 2
 
2.0%
18 1
 
1.0%
8 1
 
1.0%
ValueCountFrequency (%)
1 38
38.0%
2 18
18.0%
3 13
 
13.0%
4 8
 
8.0%
5 9
 
9.0%
6 6
 
6.0%
7 2
 
2.0%
8 1
 
1.0%
10 3
 
3.0%
18 1
 
1.0%
ValueCountFrequency (%)
142 1
 
1.0%
18 1
 
1.0%
10 3
 
3.0%
8 1
 
1.0%
7 2
 
2.0%
6 6
 
6.0%
5 9
9.0%
4 8
8.0%
3 13
13.0%
2 18
18.0%
Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.72
Minimum0
Maximum142
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:02.609037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile8.05
Maximum142
Range142
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.233323
Coefficient of variation (CV)3.826162
Kurtosis92.529654
Mean3.72
Median Absolute Deviation (MAD)1
Skewness9.4582809
Sum372
Variance202.58747
MonotonicityNot monotonic
2024-04-28T15:06:02.708248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 38
38.0%
0 18
18.0%
3 13
 
13.0%
2 10
 
10.0%
5 5
 
5.0%
6 4
 
4.0%
4 4
 
4.0%
9 2
 
2.0%
7 2
 
2.0%
18 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
0 18
18.0%
1 38
38.0%
2 10
 
10.0%
3 13
 
13.0%
4 4
 
4.0%
5 5
 
5.0%
6 4
 
4.0%
7 2
 
2.0%
8 1
 
1.0%
9 2
 
2.0%
ValueCountFrequency (%)
142 1
 
1.0%
18 1
 
1.0%
10 1
 
1.0%
9 2
 
2.0%
8 1
 
1.0%
7 2
 
2.0%
6 4
 
4.0%
5 5
 
5.0%
4 4
 
4.0%
3 13
13.0%
Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66
Minimum0
Maximum5
Zeros68
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:02.851171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.05
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.240886
Coefficient of variation (CV)1.8801303
Kurtosis4.5982163
Mean0.66
Median Absolute Deviation (MAD)0
Skewness2.2301484
Sum66
Variance1.539798
MonotonicityNot monotonic
2024-04-28T15:06:03.022647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 68
68.0%
1 16
 
16.0%
2 7
 
7.0%
3 4
 
4.0%
5 4
 
4.0%
4 1
 
1.0%
ValueCountFrequency (%)
0 68
68.0%
1 16
 
16.0%
2 7
 
7.0%
3 4
 
4.0%
4 1
 
1.0%
5 4
 
4.0%
ValueCountFrequency (%)
5 4
 
4.0%
4 1
 
1.0%
3 4
 
4.0%
2 7
 
7.0%
1 16
 
16.0%
0 68
68.0%
Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-28T15:06:03.151190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T15:06:03.236006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

Most occurring characters

ValueCountFrequency (%)
0 100
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 100
100.0%

reviews_per_month
Real number (ℝ)

MISSING 

Distinct73
Distinct (%)81.1%
Missing10
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.89744444
Minimum0.01
Maximum3.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2024-04-28T15:06:03.339341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.0345
Q10.2225
median0.62
Q31.265
95-th percentile2.649
Maximum3.8
Range3.79
Interquartile range (IQR)1.0425

Descriptive statistics

Standard deviation0.83117699
Coefficient of variation (CV)0.92615983
Kurtosis1.2703519
Mean0.89744444
Median Absolute Deviation (MAD)0.485
Skewness1.2181412
Sum80.77
Variance0.69085519
MonotonicityNot monotonic
2024-04-28T15:06:03.484933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 2
 
2.0%
1.88 2
 
2.0%
0.02 2
 
2.0%
0.57 2
 
2.0%
0.25 2
 
2.0%
0.33 2
 
2.0%
0.56 2
 
2.0%
0.12 2
 
2.0%
0.07 2
 
2.0%
0.04 2
 
2.0%
Other values (63) 70
70.0%
(Missing) 10
 
10.0%
ValueCountFrequency (%)
0.01 2
2.0%
0.02 2
2.0%
0.03 1
1.0%
0.04 2
2.0%
0.05 2
2.0%
0.07 2
2.0%
0.08 1
1.0%
0.09 1
1.0%
0.11 1
1.0%
0.12 2
2.0%
ValueCountFrequency (%)
3.8 1
1.0%
3.11 1
1.0%
3.07 1
1.0%
2.82 2
2.0%
2.44 1
1.0%
2.13 1
1.0%
2.07 1
1.0%
1.96 1
1.0%
1.9 1
1.0%
1.88 2
2.0%

Interactions

2024-04-28T15:05:38.385283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:52.951961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:56.503957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:59.598172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:02.557605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:05.403249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:08.427268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:11.424744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:14.461327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:17.417575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.632292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.935060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:27.345893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.684018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-28T15:03:56.600958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-28T15:04:10.884289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:13.943326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:16.891538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.044454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.219684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:26.695301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.074654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:33.195346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:36.437850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:39.814838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:42.893321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:45.911535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:49.271633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:52.370836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:56.134108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:00.049096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:03.223539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:06.332659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:09.354788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:12.598102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:15.788074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:18.932246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:21.996371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:25.281071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:28.460223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:31.524950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:34.641385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:37.855173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:41.109947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:56.036957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:59.151172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:02.119210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:04.959248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:07.989675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:10.982287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:14.033332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:16.982532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.137459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.310685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:26.800430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.202854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:33.301258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:36.524545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:39.910280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:42.981733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:46.002282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:49.386137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:52.459979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:56.285675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:00.147459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:03.313351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:06.418184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:09.437788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:12.699609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:15.874779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:19.022390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:22.118794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:25.383105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:28.551780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:31.618663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:34.743770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:37.944308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:41.193096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:56.126960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:59.237172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:02.200210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:05.070252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:08.068670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:11.064287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:14.115327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:17.060532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.229725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.397692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:26.907355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.308044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:33.415193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:36.609796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:40.003301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:43.062389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:46.102884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:49.481541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:52.540018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:56.400791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:00.227193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:03.403571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:06.499717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:09.515788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:12.792607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:15.952675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:19.115793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:22.207483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:25.473262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:28.634399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:31.701657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:34.836767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:38.028085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:41.284258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:56.221956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:59.332172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:02.286209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:05.156247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:08.158671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:11.148294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:14.204328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:17.147531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.334079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.540792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:27.047956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.407215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:33.515788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:36.692535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:40.103527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:43.152386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:46.197317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:49.572345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:52.634842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:56.510513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:00.320661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:03.490319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:06.587243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:09.639217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:12.887239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:16.039520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:19.205015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:22.308464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:25.566743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:28.723560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:31.795090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:34.940705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:38.120290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:41.374069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:56.319960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:59.428177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:02.374211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:05.239247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:08.253669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:11.237289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:14.291332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:17.234536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.435289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.688518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:27.166445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.500063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:33.614761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:36.784258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:40.208839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:43.244987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:46.300076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:49.671781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:52.725172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:56.636465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:00.424927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:03.578659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:06.683295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:09.729697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:12.975406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:16.131104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:19.296564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:22.412142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:25.657218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:28.816327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:31.899168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:35.049728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:38.209424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:41.456415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:56.410957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:03:59.510171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:02.455211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:05.320246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:08.340670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:11.321289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:14.376326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:17.320531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:20.524289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:23.811062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:27.253118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:30.589755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:33.734255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:36.862023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:40.303508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:43.332654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:46.395962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:49.761973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:52.805197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:04:56.736436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:00.506644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:03.657015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:06.764858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:09.813781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:13.057631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:16.214726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:19.382187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:22.492226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:25.743017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:28.894501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:31.988739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:35.147563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T15:05:38.289050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-28T15:05:41.724877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-28T15:05:42.154924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idlisting_urlscrape_idlast_scrapedsourcenamedescriptionneighborhood_overviewpicture_urlhost_idhost_urlhost_namehost_sincehost_locationhost_abouthost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_thumbnail_urlhost_picture_urlhost_neighbourhoodhost_listings_counthost_total_listings_counthost_verificationshost_has_profile_pichost_identity_verifiedneighbourhoodneighbourhood_cleansedneighbourhood_group_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbathrooms_textbedroomsbedsamenitiespriceminimum_nightsmaximum_nightsminimum_minimum_nightsmaximum_minimum_nightsminimum_maximum_nightsmaximum_maximum_nightsminimum_nights_avg_ntmmaximum_nights_avg_ntmcalendar_updatedhas_availabilityavailability_30availability_60availability_90availability_365calendar_last_scrapednumber_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dfirst_reviewlast_reviewreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuelicenseinstant_bookablecalculated_host_listings_countcalculated_host_listings_count_entire_homescalculated_host_listings_count_private_roomscalculated_host_listings_count_shared_roomsreviews_per_month
017878https://www.airbnb.com/rooms/17878202312260341382023-12-27city scrapeCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bathNaNThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.https://a0.muscache.com/pictures/65320518/30698f38_original.jpg68997https://www.airbnb.com/users/show/68997Matthias2010-01-08Rio de Janeiro, BrazilI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.within an hour100%96%thttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_mediumCopacabana2.05.0['email', 'phone']ttRio de Janeiro, BrazilCopacabanaNaN-22.96599-43.17940Entire condoEntire home/apt5NaN1 bathNaN2.0[]$1,357.005285528285.028.0NaNt57142692023-12-273112942010-07-152023-12-224.704.774.654.834.914.774.67NaNf11001.90
125026https://www.airbnb.com/rooms/25026202312260341382023-12-27city scrapeRental unit in Rio de Janeiro · ★4.72 · 1 bedroom · 1 bed · 1 bathNaNCopacabana is a lively neighborhood and the apartment is located very close to an area in Copa full of bars, cafes and restaurants at Rua Bolivar and Domingos Ferreira. Copacabana never sleeps, there is always movement and it's a great mix of all kinds of people.https://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg102840https://www.airbnb.com/users/show/102840Viviane2010-04-03Rio de Janeiro, BrazilHi guys,\n\nViviane is a commercial photographer, an avid world traveler, (a former photographer for Airbnb) and an Airbnb superhost. And a free lance photographer for other wonderful clients. She loves life and meeting people.\n\nWe work together in providing the best accommodation to people and we are\nfirm believers of enjoying the moment as a prime attitude towards life!\nwithin an hour100%80%thttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_x_mediumCopacabana1.05.0['email', 'phone']ttRio de Janeiro, BrazilCopacabanaNaN-22.97735-43.19105Entire rental unitEntire home/apt3NaN1 bathNaN1.0[]$865.002602460602.260.0NaNt318482282023-12-272752922010-06-072023-12-034.724.704.794.814.924.844.60NaNf11001.67
235764https://www.airbnb.com/rooms/35764202312260341382023-12-27city scrapeLoft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 bathsNaNOur guests will experience living with a local peole "Carioca" in a very friendly building with 24 hours a day security with all kind of stores, banks, transports, restaurants.https://a0.muscache.com/pictures/23782972/1d3e55b0_original.jpg153691https://www.airbnb.com/users/show/153691Patricia Miranda & Paulo2010-06-27Rio de Janeiro, BrazilHello, We are Patricia Miranda and Paulo.\nWe are a couple who love to meet new people, new cultures, we both are very easy going persons, We are retired after working for several years in tourism and an international airline company. We also used do host in our own residence International students from all over the world. We are gay friendly and everybody is welcome! \n\n\n\n!within an hour100%98%thttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_x_mediumCopacabana1.02.0['email', 'phone']ttRio de Janeiro, BrazilCopacabanaNaN-22.98107-43.19136Entire loftEntire home/apt2NaN1.5 bathsNaN1.0[]$373.00315167153.114.7NaNt4912622023-12-274543622010-10-032023-12-174.904.934.934.974.954.944.89NaNf11002.82
341198https://www.airbnb.com/rooms/41198202312260341382023-12-27city scrapeRental unit in Rio de Janeiro · ★4.21 · 2 bedrooms · 1 bathNaNNaNhttps://a0.muscache.com/pictures/3576716/2d6a9301_original.jpg178975https://www.airbnb.com/users/show/178975Nicky2010-07-23Rio de Janeiro, BrazilHi fellow Airbnb-ers,\r\nMr Francis , Nixx, Nicky ,Nick call me what you like\r\nOriginally from England but now in South America\r\nI love This crazy thing called life. I enjoy meeting interesting multi dimensional people from any country, class or colour ., \r\nI don't understand negative or petty people! because I believe life is just too short!\r\nI'm not the greatest tour guide, but it would be my pleasure to help you where I can.\r\nI administrate a few places on here, so feel free to take a browse of my listings.\r\nPlease remember that some of these places may be owners homes and not hotels,\r\nSo don't expect the hilton treatment as I like to keep it sweet & simple.\r\nLet me know also if the apt is different from what was listed and I will do my best to \r\nRectify the situation.\r\nRemember even though we are here to do a lil Bizniz the most important thing is to enjoy Rio de Janeiro in all it's splendour ! Rio is always an Experience!\r\nMy favourite travel hotspots outside of Brazil - Colombia or Caribbean \r\nHere is a story for you\r\nThere was a married American guy called Lance who was working abroad as a \r\ncomputer systems analyst to make ends meet.He traveled a lot for his job\r\nAnd had been doing so for the last 37 years. Last year whilst working in Thailand\r\nHe picked up a young sexy wife 23 years old she was and a real beauty! It was\r\nreally difficult for him to leave her at home, but the bills needed to be paid and \r\nbesides his wife was high maintenance.\r\n\r\nIn a particularly difficult month he received a message from his\r\nDesirable Wife who was taking care of his home" I signed up for a new service Airbnb\r\nWhere you can rent space in your house,make good money and I buy new\r\nShoes"\r\nHaving a beautiful home (and not to mention photo), it wasn't long before she received\r\nA guest, actually a handsome pilot from Greece, Adonis was his name.\r\n\r\n After 5 days Lance could hardly make contact with his wife but unexpectedly \r\n received a message from his Airbnb guest. :\r\n\r\nHi Sir,\r\nThank you, I enjoyed using your home & everything your city had to offer,\r\nI hope you won't be mad but\r\nI was using your wife...I was using day and night ...\r\nI explored immensely,\r\nI was using when you are not present at home... \r\nIn fact I was using more than You are using.....\r\n\r\nI confess this because now I feel very much guilt...\r\nAnd is not acceptable under the eyes of God\r\nHope You will accept my sincere apologies".\r\n\r\n... Lance distraught with this revelation , jumped On the next plane to his city, \r\n, entered his home tied & taped up his wife., and made her watch while he cut\r\nUp and burnt all her expensive clothes and shoes, put her in front of the mirror\r\nAnd proceeded to shear off her hair\r\n\r\nFew minutes later he received another message :\r\n\r\nSorry sir, spelling mistake ... wifi not wife.\r\n\r\n\r\n\r\n\r\n\r\nLol! \r\n\r\n\r\nwithin an hour90%89%fhttps://a0.muscache.com/im/users/178975/profile_pic/1384245754/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/178975/profile_pic/1384245754/original.jpg?aki_policy=profile_x_mediumCopacabana4.05.0['email', 'phone']ttNaNCopacabanaNaN-22.98102-43.19172Entire rental unitEntire home/apt5NaN1 bathNaNNaN[]$1,701.003365373653653.2365.0NaNt153841442023-12-2717002013-06-042016-02-094.213.884.254.694.564.444.38NaNf22000.13
4326205https://www.airbnb.com/rooms/326205202312260341382023-12-27city scrapeCondo in Rio de Janeiro · ★4.57 · 1 bedroom · 1 bed · 1 bathNaNNaNhttps://a0.muscache.com/pictures/c550151d-910c-40c6-96a8-d2a8bd770361.jpg1603206https://www.airbnb.com/users/show/1603206Bob2012-01-13Rio de Janeiro, BrazilI'm originally from the US, but moved to this Great City of\n Rio de Janeiro.within a few hours100%93%fhttps://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_x_mediumCopacabana7.07.0['email', 'phone']tfNaNCopacabanaNaN-22.96825-43.18237Entire condoEntire home/apt4NaN1 bathNaN1.0[]$366.003180331801803.0180.0NaNt622272932023-12-271521402012-04-182023-11-214.574.724.464.834.774.834.59NaNf55001.07
5326575https://www.airbnb.com/rooms/326575202312260341382023-12-27city scrapeRental unit in Rio de Janeiro · ★4.81 · 2 bedrooms · 3 beds · 2 bathsNaNCome to stay in Baixo Copa, the more trendy and happy neighborhood of all Rio de Janeiro, in the heart of Copacabana, less than a half block from the beach. Restaurants, bars, grocery stores, theaters, banks, hotels and tourism agencies are in the neighborhood.https://a0.muscache.com/pictures/4cffcbcf-16c2-4624-afee-29a7ffe20698.jpg1668565https://www.airbnb.com/users/show/1668565Maria José2012-01-29Rio de Janeiro, BrazilMy name is Maria José, born in 1977 in Brazil. \n\nHoping to meet you,\n\nMaria Joséwithin a few hours100%69%thttps://a0.muscache.com/im/pictures/user/f52d7397-5f15-4876-a275-bc0ad10c9cb8.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/f52d7397-5f15-4876-a275-bc0ad10c9cb8.jpg?aki_policy=profile_x_mediumCopacabana1.08.0['email', 'phone']ttRio de Janeiro, BrazilCopacabanaNaN-22.97696-43.18933Entire rental unitEntire home/apt5NaN2 bathsNaN3.0[]$368.004604460604.060.0NaNt49392452023-12-272271202012-03-192023-11-214.814.854.804.914.894.954.73NaNf11001.58
6216461https://www.airbnb.com/rooms/216461202312260341382023-12-26city scrapeRental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bathNaNNaNhttps://a0.muscache.com/pictures/2628485/1ed768bb_original.jpg1154263https://www.airbnb.com/users/show/1154263Zeilma , Da2011-09-13Rio de Janeiro, BrazilOlá meu nome é Zeilma, moro no Rio de Janeiro. Sou casada, não tenho filhos. Será um prazer receber a sua visita!a few days or more0%NaNfhttps://a0.muscache.com/im/users/1154263/profile_pic/1336700167/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/1154263/profile_pic/1336700167/original.jpg?aki_policy=profile_x_mediumFlamengo1.01.0['email', 'phone']ttNaNFlamengoNaN-22.93990-43.17676Private room in rental unitPrivate room2NaN1 bathNaN1.0[]$734.001760117607601.0760.0NaNt3060903652023-12-26000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNf1010NaN
748305https://www.airbnb.com/rooms/48305202312260341382023-12-26city scrapeRental unit in Ipanema · ★4.76 · 6 bedrooms · 7 beds · 7 bathsNaNEnter Bossa Nova history by staying in the very street where real-life 'Girl From Ipanema' inspired Vinicius de Moraes to write the worldwide famous song, "each day when she walks to the sea".<br /><br />Located seconds from Ipanema Beach's best spot Posto 9, on the first beach block in the heart of Ipanema’s very best neighbourhood, enjoy staying in this elegant and modern hideaway in a peaceful residential street.<br /><br />Ipanema and Leblon are by far the safest locations in Rio, with popular restaurants, cafes and boutique stores walking distance to the apartment.https://a0.muscache.com/pictures/miso/Hosting-48305/original/cce14bf9-c5b6-44c6-a89f-61323975afdb.jpeg70933https://www.airbnb.com/users/show/70933Goitaca2010-01-16Rio de Janeiro, BrazilA new frontier of hospitality\n\nThe word meaning ‘Nomad’ in the native language of Tupi Guarani, Goitaca symbolizes a new genre of travel, one built with the modern traveler in mind. With locally-rooted and experience-driven stays in homes that are intentionally designed, fully-equipped, and remote-work ready, we’re driven to shift the role of hospitality in the world. Bringing the comfort of a hotel together with the privacy of a home, our residences and customized care provide unique experiences for our guests. \n\nExperience-driven stays that nurture our guests’ well-being \n\nThe well-being and satisfaction of our guests are our utmost focus and we are determined to continue improving the quality and depth of the experiences and stories we’re creating. We believe in delivering remarkable hospitality that is also accessible. By designing, developing, and operating our residences ourselves, we provide unique, personalized stays at a fraction of the cost and pass those savings back to our guests.\n\nA tribute to Brazil\n\nOur wish is that our guests may discover and experience the magic, beauty, and ***alegria*** of Rio, while paying homage to its history and multi-faceted culture.\n\nBehind Goitaca stands a small and dedicated local team who take pride in going the extra mile to ensure our guests live an easy, authentic, and unforgettable Rio story. Working together since 2011, we have been lucky to welcome over 2,000 guests and continue to connect and learn from them.within a few hours100%96%thttps://a0.muscache.com/im/pictures/user/c2d77835-fab6-45f9-a3ef-ef570dda44a5.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/c2d77835-fab6-45f9-a3ef-ef570dda44a5.jpg?aki_policy=profile_x_mediumIpanema12.033.0['email', 'phone', 'work_email']ttIpanema, Rio de Janeiro, BrazilIpanemaNaN-22.98591-43.20302Entire rental unitEntire home/apt13NaN7 bathsNaN7.0[]$6,604.002892589892.389.0NaNt1838653192023-12-261633632011-03-022023-12-104.764.724.704.834.834.944.59NaNt109101.04
8216700https://www.airbnb.com/rooms/216700202312260341382023-12-28city scrapeRental unit in Rio de Janeiro · ★4.96 · 1 bedroom · 1 bed · 1 shared bathNaNO bairro de Laranjeiras é bem residencial e arborizado. Fica na Zona Sul do Rio de Janeiro, bem próximo das praias e do Centro da cidade. Na principal rua do bairro, Rua das Laranjeiras, tem ônibus que leva aos principais bairros da cidade e muito pontos turísticos. A minha rua fica distante 20 minutos a pé da estação do metro Largo do Machado. Vocês podem ir andando, 10 minutos, para a estação do trem do Corcovado que leva ao Cristo Redentor. A minha rua, Rua General Glicério, é a rua mais linda do bairro e fica em um recanto que parece uma bairro dentro do bairro. É uma rua bem segura e aos sábados tem feira livre com música ao vivo na praça. Em frente ao meu prédio tem padaria, pequenos restaurante e mini-mercados. Na rua principal, esquina com a minha rua tem bancos, supermercados, farmácias, pontos de taxi e todos os outros serviços.https://a0.muscache.com/pictures/6162310/be07750f_original.jpg1118486https://www.airbnb.com/users/show/1118486Moara2011-09-06Rio de Janeiro, BrazilSou formada em Engenharia na UFRJ, Analise de Sistemas na PUC e trabalhei 20 anos com Marketing.\r\nMeu marido é musico e compositor. Atualmente estou trabalhando como empresaria e produtora cultural e o resultado principal desse trabalho foi o lançamento do primeiro CD do meu marido.\r\nTrabalho também com consultoria na área de marketing e adoro fotografia.\r\nTenho uma filha cineasta e um filho jornalista, que moram sozinhos, e o meu filho mais jovem ainda vive comigo e meu marido.\r\nAdoramos curtir os amigos, cinema, teatro, shows, etcwithin a few hours90%100%fhttps://a0.muscache.com/im/users/1118486/profile_pic/1338861150/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/1118486/profile_pic/1338861150/original.jpg?aki_policy=profile_x_mediumLaranjeiras3.05.0['email', 'phone']ttRio de Janeiro, BrazilLaranjeirasNaN-22.94373-43.19147Private room in rental unitPrivate room4NaN1 shared bathNaN1.0[]$300.003303330303.030.0NaNt2451813562023-12-2824302012-06-182023-11-054.964.914.865.005.004.914.77NaNf20200.17
9219250https://www.airbnb.com/rooms/219250202312260341382023-12-26city scrapeLoft in Rio de Janeiro · ★4.82 · 1 bedroom · 2 beds · 1 bathNaNNaNhttps://a0.muscache.com/pictures/60226390/d079690d_original.jpg1134264https://www.airbnb.com/users/show/1134264Ricardo2011-09-09Rio de Janeiro, BrazilSou designer e, desenvolvo pesquisas na área de construção de embarcações artesanais. Sou casado e minha companheira se chama Rachel. Para mim é sempre um prazer receber pessoas em minha casa, pois trocamos experiências e ampliamos a nossa rede de amigos.within an hour100%100%thttps://a0.muscache.com/im/pictures/user/2ab75f2d-15d0-4d01-a565-4d1483fed334.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/2ab75f2d-15d0-4d01-a565-4d1483fed334.jpg?aki_policy=profile_x_mediumSanta Teresa2.02.0['email', 'phone', 'work_email']ttNaNSanta TeresaNaN-22.91666-43.17947Entire loftEntire home/apt4NaN1 bathNaN2.0[]$254.0023025112511252.11125.0NaNt59373122023-12-264312522012-06-112023-12-114.824.874.744.944.874.764.78NaNt22003.07
idlisting_urlscrape_idlast_scrapedsourcenamedescriptionneighborhood_overviewpicture_urlhost_idhost_urlhost_namehost_sincehost_locationhost_abouthost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_thumbnail_urlhost_picture_urlhost_neighbourhoodhost_listings_counthost_total_listings_counthost_verificationshost_has_profile_pichost_identity_verifiedneighbourhoodneighbourhood_cleansedneighbourhood_group_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbathrooms_textbedroomsbedsamenitiespriceminimum_nightsmaximum_nightsminimum_minimum_nightsmaximum_minimum_nightsminimum_maximum_nightsmaximum_maximum_nightsminimum_nights_avg_ntmmaximum_nights_avg_ntmcalendar_updatedhas_availabilityavailability_30availability_60availability_90availability_365calendar_last_scrapednumber_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dfirst_reviewlast_reviewreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuelicenseinstant_bookablecalculated_host_listings_countcalculated_host_listings_count_entire_homescalculated_host_listings_count_private_roomscalculated_host_listings_count_shared_roomsreviews_per_month
9097636https://www.airbnb.com/rooms/97636202312260341382023-12-28city scrapeHome in Rio de Janeiro · ★4.62 · 1 bedroom · 2 beds · 1 shared bathNaNNaNhttps://a0.muscache.com/pictures/12339607/212f6773_original.jpg496656https://www.airbnb.com/users/show/496656Marcia Longras2011-04-09Rio de Janeiro, BrazilI'm a Brazilian lady, I was born in Rio de Janeiro, the wonderful city!\n I like to dance , travel, walk..\nI love to host!\r\nI enjoy Airbnb.\r\n\r\n Marcia Longras .within an hour100%78%fhttps://a0.muscache.com/im/pictures/user/User-496656/original/ef187b44-0111-45bc-acf4-ac05479390a2.jpeg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/User-496656/original/ef187b44-0111-45bc-acf4-ac05479390a2.jpeg?aki_policy=profile_x_mediumBotafogo6.013.0['email', 'phone']ttNaNBotafogoNaN-22.94169-43.18703Private room in homePrivate room2NaN1 shared bathNaN2.0[]$540.0078520787852052078.0520.0NaNt3060903652023-12-2817002012-01-042021-12-154.624.434.364.864.794.434.29NaNf63300.12
91272631https://www.airbnb.com/rooms/272631202312260341382023-12-27city scrapeRental unit in Rio de Janeiro · ★5.0 · 1 bedroom · 2 beds · 1 shared bathNaNNaNhttps://a0.muscache.com/pictures/airflow/Hosting-272631/original/75b64c26-29b7-4e46-b9d0-c9f3cf6bcb86.jpg1426830https://www.airbnb.com/users/show/1426830Neyde2011-11-20Rio de Janeiro, BrazilSOU PROFESSORA DE HISTÓRIA. \r\nADORO MÚSICA E LIVROS. GOSTO MUITO DE VIAJAR E DE PREPARAR E SABOREAR BONS PRATOS.\r\n\r\nOBS. Falamos um pouco de inglês e francêswithin a few hours90%95%fhttps://a0.muscache.com/im/pictures/user/63796636-be5f-4527-ac48-aee836bd6dbb.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/63796636-be5f-4527-ac48-aee836bd6dbb.jpg?aki_policy=profile_x_mediumCopacabana4.07.0['email', 'phone']ttNaNCopacabanaNaN-22.96750-43.18666Private room in rental unitPrivate room2NaN1 shared bathNaN2.0[]$729.0023022112511252.01125.0NaNt2454842642023-12-2729402014-07-012023-11-055.005.004.934.935.004.974.97NaNt31200.25
92273261https://www.airbnb.com/rooms/273261202312260341382023-12-26city scrapeRental unit in Rio de Janeiro · 3 bedrooms · 3 beds · 3.5 bathsNaNNaNhttps://a0.muscache.com/pictures/4487816/a8d428ee_original.jpg533566https://www.airbnb.com/users/show/533566Lucas2011-04-25Rio de Janeiro, BrazilHello , my name is Lucas , I'm a independent computer artist . I have this amazing apartment at wonderful city , Rio de Janeiro .within a few hours100%NaNfhttps://a0.muscache.com/im/pictures/user/25dc6f05-635d-45b9-b4c1-87fb8e2252a4.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/25dc6f05-635d-45b9-b4c1-87fb8e2252a4.jpg?aki_policy=profile_x_mediumLeblon1.02.0['email', 'phone']tfNaNLeblonNaN-22.98632-43.22936Entire rental unitEntire home/apt6NaN3.5 bathsNaN3.0[]$4,892.002722772.07.0NaNt3060903652023-12-26000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNf1100NaN
93400326https://www.airbnb.com/rooms/400326202312260341382023-12-27city scrapeRental unit in Rio de Janeiro · ★4.29 · 2 bedrooms · 4 beds · 1 bathNaNNaNhttps://a0.muscache.com/pictures/miso/Hosting-400326/original/2d72c30d-a810-4a03-bc3e-ed444ff1a41e.jpeg1998370https://www.airbnb.com/users/show/1998370Jean2012-03-24Rio de Janeiro, BrazilI am a young Brazilian who loves to travel and learning about new cultures.\r\nI know many places in Brazil and I have been traveling through out South America, United States, Canada, Australia and many other places\r\nAs I do not do this every day, I feel very happy to receive people in my town who are going through this experience!\r\n\r\nBased on my experiences around the planet, I just better prepared this apartment in Copacabana point to ensure the success and happiness of the people who come to visit Rio de Janeiro spend a good time!\r\n\r\nPlease feel free to get in touch, I'm more than happy to discuss any travel plans you may Possible have in the locality.\r\nYou are more than welcome already!\r\n\r\n\r\n\r\n"It was very great to stay stay some nights at Cool & Cozy Appartment! It was really nice and the location was the best at in Rio! I liked most Copacobana with its beach, small stores and eating-drinking possibilities. Everything was easy and very comfortable." \r\nVivika\r\n\r\n"El departamento está en una excelente ubicación! todo a la mano, cerca del subte, de la playa y con muchos colectivos sobre Barata Riveiro!!"\r\nKarina\r\n\r\nExcelente ubicacion del apartamento, acceso a mercados, tiendas, bares. Apartamento comodisimo, acogedor, limpio, una experiencia gratificante...\r\nNatalywithin a few hours90%57%NaNhttps://a0.muscache.com/im/pictures/user/66ea20d2-7b42-45c3-ade1-ec3593460f92.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/66ea20d2-7b42-45c3-ade1-ec3593460f92.jpg?aki_policy=profile_x_mediumCopacabana8.08.0['email', 'phone']ttNaNCopacabanaNaN-22.96714-43.18372Entire rental unitEntire home/apt6NaN1 bathNaN4.0[]$1,383.001901190901.090.0NaNt224543292023-12-27155102012-05-132023-08-264.294.254.014.754.764.864.19NaNf55001.10
9498969https://www.airbnb.com/rooms/98969202312260341382023-12-26city scrapeHome in Rio de Janeiro · ★4.79 · 1 bedroom · 2 beds · 1.5 bathsNaNNaNhttps://a0.muscache.com/pictures/airflow/Hosting-98969/original/54f1c924-6822-4c61-a93b-2160e0cd91b4.jpg521613https://www.airbnb.com/users/show/521613Cyro2011-04-19Rio de Janeiro, BrazilOla , meu nome é Cyro e tenho ótimos espaços para alugar na cidade maravilhosa do Rio de Janeiro ,venha conhecer !within an hour100%91%fhttps://a0.muscache.com/im/users/521613/profile_pic/1303253512/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/521613/profile_pic/1303253512/original.jpg?aki_policy=profile_x_mediumRecreio dos Bandeirantes5.05.0['email', 'phone']tfNaNRecreio dos BandeirantesNaN-23.03154-43.47437Private room in homePrivate room2NaN1.5 bathsNaN2.0[]$267.002302230302.030.0NaNt2237671572023-12-26482422012-01-182023-12-174.794.734.484.854.964.944.79NaNf41300.33
95404674https://www.airbnb.com/rooms/404674202312260341382023-12-27previous scrapeRental unit in Rio de Janeiro · ★4.87 · 1 bedroom · 3 beds · 2.5 shared bathsNaNNaNhttps://a0.muscache.com/pictures/4946952/f1288650_original.jpg261954https://www.airbnb.com/users/show/261954Elisa2010-10-14Rio de Janeiro, BrazilWe're a very, VERY happy and mix couple: half from Buenos Aires, Argentina, and half (may be the best one!) from Rio de Janeiro. This is the city that we choose for live on it! I'm a spanish teacher and my husband, Marcelo, is a musician, a flute player. So, it's easy to imagine that music (the good one, of any style) is a very important part of our routines! \n\nOur interests are traveling, cinema and good music!\n\nWe love to make new friends and to know different cultures. \n\nStay with us…and feel so welcome at our nest! We hope to have you here at our place!!! :-)within a few hours100%67%fhttps://a0.muscache.com/im/pictures/user/2b48ea47-c47f-460c-8a89-50f452a2a9ba.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/2b48ea47-c47f-460c-8a89-50f452a2a9ba.jpg?aki_policy=profile_x_mediumFlamengo3.03.0['email', 'phone', 'work_email']tfNaNFlamengoNaN-22.93974-43.17496Private room in rental unitPrivate room3NaN2.5 shared bathsNaN3.0[]$306.0030903030909030.090.0NaNt2730302023-12-27115002012-05-112017-03-024.874.874.904.904.984.834.79NaNf31200.81
96273333https://www.airbnb.com/rooms/273333202312260341382023-12-28city scrapeGuest suite in Rio de Janeiro · ★4.86 · 1 bedroom · 1 bed · 1 private bathNaNO Jardim Botânico é um bairro lindo! Fica entre a Floresta da Tijuca, maior floresta urbana do mundo, e a Lagoa Rodrigo de Freitas. A suite está localizada a um quarteirão do parque do Jardim Botânico, que é um passeio maravilhoso e a 2 quarteirões da Lagoa. Chegando na Lagoa tem-se uma vista linda, que vai mudando conforme se dá a volta nela, a volta completa tem 7,5 km. Caminhando pela Lagoa chega-se a Ipanema, pra um banho de mar, em meia hora! E a noite, na Lagoa, tem várias opções de bares e restaurantes. No Jardim Botânico tem supermercado, farmácia, banco, correio, loja, padaria, etc... Fora todos os bares, restaurantes e lugares deliciosos pra café da manhã. E ainda tem muito verde! Com 40 minutos de caminhada pode-se tomar um banho de cachoeira no meio da Floresta da Tijuca, é de tirar o fôlego!https://a0.muscache.com/pictures/3200821/8fd48336_original.jpg625565https://www.airbnb.com/users/show/625565Kakau2011-05-25Rio de Janeiro, BrazilSou pintora e designer, morei 9 anos na Europa e atualmente vivo no Rio, fonte de inspiração para minha arte. Pinto a cidade maravilhosa e assino uma linha de produtos inspirada em suas cores, relevos e calçadas.\nMoro no Jardim Botânico, um dos bairros mais charmosos do Rio, colado na Floresta da Tijuca, a 2 quarteirões da Lagoa Rodrigo de Freitas, logo em baixo do sovaco do Cristo.\nAs suites que alugo são independentes com privacidade total.within a few hours90%56%fhttps://a0.muscache.com/im/users/625565/profile_pic/1306358577/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/625565/profile_pic/1306358577/original.jpg?aki_policy=profile_x_mediumJardin Botânico2.03.0['email', 'phone']ttRio de Janeiro, BrazilJardim BotânicoNaN-22.96612-43.21899Private room in guest suitePrivate room2NaN1 private bathNaN1.0[]$193.003112533112511253.01125.0NaNt0002402023-12-2824002012-01-062022-04-044.864.834.715.005.004.744.45NaNf20200.16
9799547https://www.airbnb.com/rooms/99547202312260341382023-12-26city scrapeRental unit in Rio de Janeiro · ★4.58 · Studio · 2 beds · 1 bathNaNNaNhttps://a0.muscache.com/pictures/01c55423-2959-426f-a476-cbd1bab93d98.jpg524101https://www.airbnb.com/users/show/524101Fabiana2011-04-20Rio de Janeiro, BrazilNaNwithin a day100%88%fhttps://a0.muscache.com/im/pictures/user/e9fb3c93-c035-401f-bb5a-b5dcc9f19381.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/e9fb3c93-c035-401f-bb5a-b5dcc9f19381.jpg?aki_policy=profile_x_mediumLapa2.031.0['email', 'phone']ttNaNSanta TeresaNaN-22.91491-43.18296Entire rental unitEntire home/apt3NaN1 bathNaN2.0[]$381.0028933112511253.01125.0NaNt3054841742023-12-2666302013-03-102023-11-064.584.754.664.844.834.704.56NaNt11000.50
9899971https://www.airbnb.com/rooms/99971202312260341382023-12-28city scrapeLoft in Rio de Janeiro · ★4.79 · Studio · 3 beds · 1 bathNaNNaNhttps://a0.muscache.com/pictures/670706/0f35611d_original.jpg525976https://www.airbnb.com/users/show/525976Ricardo2011-04-21NaNI am an independent professional from Rio de Janeiro, Brazil, working and living in many different parts of the globe\r\nSou um profissional autônomo, viajo muito pelo mundo a trabalho, sou carioca e visito sempre o Rio.within an hour90%100%thttps://a0.muscache.com/im/pictures/user/2a1db51e-0495-4fef-8500-c6494406669e.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/2a1db51e-0495-4fef-8500-c6494406669e.jpg?aki_policy=profile_x_mediumVidigal1.03.0['email', 'phone', 'work_email']ttNaNLeblonNaN-22.98812-43.22897Entire loftEntire home/apt5NaN1 bathNaN3.0[]$446.003112535112511253.01125.0NaNt415342682023-12-281572212011-08-092023-12-014.794.724.664.884.884.574.68NaNf11001.04
99273363https://www.airbnb.com/rooms/273363202312260341382023-12-26city scrapeHome in Rio de Janeiro · ★5.0 · 1 bedroom · 1 bed · 1 private bathNaNSanta Teresa é um lugar único e muito interessante no Rio de Janeiro. Está situada na região central e no alto de uma colina oferecendo uma vista privilegiada da cidade, da Baia de Guanabara e do Pāo de Açúcar. Este bairro parece ter parado no tempo, com as suas casas antigas, palacetes, ruas estreitas com paralelepípedos e o famoso bondinho que leva os moradores e turistas ladeira abaixo.https://a0.muscache.com/pictures/miso/Hosting-273363/original/af4b87af-1eac-4200-87de-7c8d6cceab52.jpeg556738https://www.airbnb.com/users/show/556738Casa Da Carmen E2011-05-04Rio de Janeiro, BrazilSomos um casal, Carmen e Fernando, ,que desde 1998 compartilhamos a nossa casa localizada em Santa Teresa, próximo ao Largo do Curvelo. Priorizamos a empatia e o respeito às diferenças! Como cariocas sentimos felicidade em divulgar a vida cultural e sugerir passeios na cidade. A casa possui um terraço com uma pequena piscina e uma vista panorâmica do centro da cidade e da Baía de Guanabara. Os quartos possuem cama de solteiro ou cama de casal, banheiro privativo, além de uma cozinha totalmente equipada para uso exclusivo dos moradores. Estão incluídos nos serviços oferecidos a internet wi-fi! É fácil chegar na casa, que está localizada a 4Km do Aeroporto Santos Dumont(vôos domésticos) com conexão de ônibus para o aeroporto Internacional (20 Km) e a 7Km do terminal interestadual de ônibus. Além disso, estamos a 10 minutos caminhando até a Lapa, bairro da boemia carioca, com vários opções de música e restaurantes, e a 10 minutos a pé da estação Glória do Metrô, de onde é possível chegar em Copacabana e Ipanema em 15 minutos. Próximo a casa, temos supermercado, restaurantes, farmácia e banco 24 horas. Ficaremos muito felizes em recebê-lo(a).within a few hours100%25%fhttps://a0.muscache.com/im/pictures/user/694631f0-c779-476f-a57b-b08b7c14a53d.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/694631f0-c779-476f-a57b-b08b7c14a53d.jpg?aki_policy=profile_x_mediumSanta Teresa5.06.0['email', 'phone']ttRio de Janeiro, BrazilSanta TeresaNaN-22.91825-43.17872Private room in homePrivate room1NaN1 private bathNaN1.0[]$60.0030112530301125112530.01125.0NaNt0002692023-12-264002015-03-112017-12-045.005.005.005.005.004.754.75NaNf50500.04